Precipitation

Urbanization is often assumed to intensify precipitation and concentrate flood‐relevant extremes in city centers, yet clear observational evidence remains limited. Here, we analyze warm‐season sub‐daily precipitation during 2000–2024 across 20 urban agglomerations and 61 representative cities in China, partitioning built‐up areas into three urban‐intensity zones based on impervious surface fraction. Total precipitation generally increases more rapidly in urban areas than in nearby non‐urban reference regions, with about 75% of urban agglomerations showing faster urban wetting. However, this enhancement is primarily driven by light‐to‐moderate precipitation, for which urbanization effects strengthen systematically from weakly to highly urbanized zones. Quantitatively, urban‐intensity sensitivity is positive for non‐extreme precipitation, with urbanization effects increasing by about 0.1–0.7 mm yr −1 for each 10% increase in impervious surface fraction, but it becomes negative for extremes, decreasing on average by about 0.5–1.1 mm yr −1 . Accordingly, only 20%–25% of urban agglomerations show stronger amplification of extreme precipitation in highly urbanized zones, whereas roughly one‐third show weaker or negative urbanization effects in these zones. These patterns are consistent at the city scale, are more evident at hourly than daily resolution, and are strongest in humid regions. Our results show that stronger urbanization amplifies regional precipitation contrasts but does not systematically amplify precipitation extremes in urban cores, challenging a common assumption in urban flood‐risk assessment and emphasizing the need to distinguish general urban wetting from changes in short‐duration hazardous rainfall.

This study investigated the roles of two different ensemble-boosting approaches in numerical weather modeling (i.e., storm transposition and time-lagged approaches) for estimating worst-case precipitation scenarios associated with mesoscale convective systems (MCSs). As a case study, we focus on a record-breaking extreme precipitation event that occurred in Kumamoto, central Kyushu, Japan, on 10 August 2025. All numerical experiments were conducted using the Cloud Resolving Storm Simulator (CReSS). Time-lagged ensembles were generated by varying initialization times, while storm transposition ensembles were produced by meridionally shifting the background atmospheric fields in the control (CTL) runs at 0.05° increments. Our analysis revealed that both the storm transposition and time-lagged ensemble approaches produced significantly intensified maximum precipitation scenarios over the target watersheds. However, these scenarios exhibited distinct differences in precipitation characteristics and environmental drivers, reflecting the fundamental conceptual divergence between the two methods. Storm transposition not only shifted the spatial distribution of rainbands but also enhanced low-level convergence through interactions with the complex topography of western Kyushu. In contrast, time-lagged ensembles produced diverse spatiotemporal patterns by sampling initial condition uncertainties, leading to variations in convection initiation and rainfall duration. These results indicate that worst-case precipitation estimates are highly sensitive to the choice of ensemble generation. While storm transposition captures deterministic shifts in meso- α -scale features, time-lagged ensembles represent internal chaotic evolution. Therefore, considering both spatial displacement and initial condition uncertainty is important for a more comprehensive understanding of the upper bounds of extreme precipitation.

Over the last few years, significant progress has been made in the development of convection-permitting climate models (CPCMs), especially in improving precipitation estimates in regions with complex terrain. Recently, the South American Affinity Group (SAAG) developed a novel high-resolution dataset – hereafter referred to as the WRF-SAAG dataset – by dynamically downscaling the ERA5 reanalysis using the Weather Research and Forecasting (WRF) model over South America for the period 2000–2021. Here, we evaluate WRF-SAAG outputs across continental Chile and compare them with two widely used gridded meteorological products, CR2MET and RF-MEP, which combine reanalysis data with in situ measurements. In particular, we analyze spatial differences among climatological estimates from the three products, and we assess the quality of daily precipitation and daily maximum and minimum temperature simulations using observations from meteorological stations over the period April/2001–March/2018. We also compare the potential of these climate datasets for hydrological modeling using a conceptual, bucket-style rainfall-runoff model, evaluating the results against streamflow observations, satellite remote sensing (SRS) products, and a snow water equivalent (SWE) reanalysis product. We found that the detection accuracy varied within each Chilean macrozone, with worse performance in the Far North (17.5–26° S) and Patagonia (43.7–56° S). The evaluation of daily precipitation against station observations using Tang's Kling–Gupta efficiency (KGE T ) reveals that all datasets performed better in reproducing precipitation in rainy regions (median KGE T ≥ 0.65 in the Southern macrozone), while in arid areas such as the Near North during summer, the median KGE T was negative. The CR2MET product consistently provided the best performance for extreme precipitation and temperature, partly because it includes information from the stations used for evaluation. Finally, the application of the hydrological model shows that although WRF-SAAG simulations achieved runoff estimations comparable to the best observation-based gridded products (i.e., CR2MET or RF-MEP), with the best results in the southern macrozone they provided relatively lower performance in evapotranspiration (ET) and soil moisture estimates, as well as SWE simulations of competitive quality. More broadly, the results…

The French Alps, characterised by complex topography and diverse climatic influences, are highly vulnerable to extreme precipitation events, which can trigger floods and landslides with significant socio-economic impacts. While local stakeholders report a perceived increase in sub-daily extreme precipitation events, such a trend has not yet been statistically confirmed based on observations. In this study, we apply extreme value theory to long time series of in situ observations of daily and hourly precipitation to provide an updated climatology and trend analysis of precipitation extremes across the region. We focus on 20-year return levels to analyse rare, potentially high-impact events and show that precipitation extremes exhibit distinct spatial patterns with marked differences between daily and hourly timescales. By examining trends at monthly and seasonal levels and making use of an existing weather pattern classification, we gain a deeper understanding of the dynamics underlying these trends. For extremes of daily precipitation intensity, we observe strong positive trends in the southern French Alps and negative trends in the north, primarily driven by a stronger influence of southerly (Mediterranean) flows in autumn. The spatial patterns for hourly extremes show more spatially variable trends, and seem to be primarily influenced by local thermodynamic effects potentially linked to e.g. changing moisture availability or changes in surface albedo due to shorter snow cover persistence. This detailed analysis provides important insights for stakeholders who are working on prevention and protection from precipitation-related hazards, underscoring the need for adaptive strategies in response to changing precipitation patterns. It also highlights the importance of region-specific and timescale-specific approaches in future studies of precipitation extremes.

Spatially resolved, multi-year observations of stable water isotopes in precipitation remain scarce for high-relief maritime islands. We present a historical dataset of monthly precipitation isotopes collected between September 2000 and December 2003 at 15 stations on Jeju Island, South Korea, spanning elevations of 5–1230 ma.s.l. The dataset contains 462 paired δ 18 O and δ 2 H measurements, deuterium excess ( d -excess = δ 2 H − 8 × δ 18 O), station and sampling metadata, and 488 station-month records of precipitation and air-temperature estimates, including 26 records without corresponding isotope measurements. Missing isotope observations were not imputed. Individual δ 18 O and δ 2 H values range from −13.2 ‰ to −2.0 ‰ and from −93.3 ‰ to −5.8 ‰, respectively, and generally exhibit lower values in summer than during the cool season. Unweighted station means for the common 2002–2003 period yield elevation gradients of −0.146 ± 0.013 ‰ per 100 m for δ 18 O and −1.043 ± 0.128 ‰ per 100 m for δ 2 H, with uncertainties expressed as standard errors. An island–nearshore comparison with isotope-enabled model output provides context for broad seasonal variability. By recovering and openly publishing observations collected more than two decades ago, this dataset preserves a historical baseline for subsequent precipitation-isotope monitoring and hydrological studies on Jeju Island. The data are available at https://doi.org/10.22747/paper_data.20260219.42 (Lee et al., 2026).

The 18 July 1942 Smethport, Pennsylvania storm produced US and world record rainfall accumulations of 165 mm in 10 min, 240 mm in 25 min and 780 mm in 270 min. Observations from the Smethport storm control sub‐daily Probable Maximum Precipitation (PMP) estimates over a large region of the eastern US. The 18 July 1942 storm is the archetypal example of a Smethport‐Like storm. A prominent feature of Smethport‐Like storms is that they are Northwest Flow events, a class of storm systems linked to development of the summer ridge over the US. The Northwest Flow setting for these storms results in characteristic properties of space‐time variability of rainfall extremes. Polarimetric radar analyses for recent Smethport‐Like storms illustrate the evolution of mesoscale convective systems producing extreme rainfall and provide insights into physical processes associated with peak rainfall rates from the Smethport storm. Smethport‐Like storms are on the extreme high end of the convective intensity spectrum, with storm rotation often contributing to large, intense updrafts. Synoptic scale, mesoscale and storm‐scale properties of Smethport‐Like storms combine to create a near‐optimal environment for extremes of water vapor convergence, a principal ingredient of record, short‐duration rainfall. The central element of the Smethport Challenge is to accurately simulate the climatology of rainfall extremes for Smethport‐Like storms using convection‐permitting climate models.

Excess water during the planting season is a growing source of production and income risk for agriculture. Yet its impacts remain poorly quantified. Existing studies typically rely on precipitation‐based indicators and therefore miss how soil saturation, runoff dynamics, and adaptive infrastructure shape crop failure. This paper fills that gap by integrating high‐resolution hydrologic model outputs with county‐level reports of failed and prevented planting across the U.S. Midwest from 2009 to 2020. We construct process‐based measures of runoff and soil moisture and evaluate their ability to explain losses while explicitly accounting for the presence of subsurface drainage systems. Hydrologic indicators strongly predict planting‐season losses: one additional day above the 95th‐percentile runoff threshold was associated with increase in failed or prevented acres by 0.37–0.42 percent. Counties with extensive artificial drainage see up to a 4.38 percent reduction in failed acres (up to 87% reduction in planting risk). Aggregated across the region, subsurface drainage preserved an estimated $13.53 billion in avoided agricultural losses over the study period. The results demonstrate that model‐derived excess‐water metrics outperform precipitation in most model setups. In particular, runoff‐based measures become especially informative when accounting for interactions with subsurface drainage infrastructure, highlighting how hydrologic context shapes crop susceptibility to excess water. The findings show the value of integrating hydrologic processes and adaptive infrastructure into agricultural risk assessment within coupled human–environment systems. Despite the value of drainage demonstrated here, future integrative work must consider potential tradeoffs imposed by nitrate concentrations and export.

Atmospheric rivers (ARs) play a crucial role in the global hydrological cycle, yet how the intrinsic midlatitude atmospheric variability modulates their activity on subseasonal timescales remains poorly understood. Here, we show that AR frequency and landfall in both hemispheres vary strongly with the strength and phase of the Baroclinic Annular Mode (BAM). During its positive phase, BAM increases AR occurrence by up to ∼50% and AR‐driven precipitation extremes by up to ∼40% relative to climatology. Stronger and more frequent ARs during the positive phase of the BAM are primarily driven by enhanced potential‐to‐kinetic energy conversion, which favors AR growth and intensification, particularly in the storm‐track regions. This response is consistent with dynamically enhanced poleward moisture transport due to a more baroclinically unstable and stormier environment. The results underscore the importance of BAM‐related intrinsic midlatitude storm‐track variability in modulating global AR activity and its potential to improve extended‐range AR prediction on subseasonal timescales.

The instantaneous unit hydrograph (IUH) synthetically describes how effective rainfall is transformed into discharge at the catchment outlet. According to classical geomorphological theories, the IUH is regarded as a time‐invariant probability density function of watershed response times, an interpretation that implicitly postulates a fixed configuration of the channel network. However, river networks dynamically expand and contract, leading to systematic temporal variations in response times that challenge the foundations of standard IUH approaches. Here, we develop a generalized unit hydrograph theory that explicitly accounts for time‐varying river networks. The proposed framework introduces a dynamic formulation of the convolution relationship linking effective rainfall and streamflow, applicable when active network length varies during and across events. In this case, the rainfall–runoff kernel can be decomposed into two components: the classical IUH contribution evaluated on the instantaneous network configuration and an additional dynamic term capturing the effects of temporal changes in flowing stream length. Numerical applications show that, across the explored configurations, network dynamics reshape the hydrological response of catchments and the associated flow‐duration curves, in particular by increasing flood peaks and slowing down recessions. This pattern is confirmed by a proof‐of‐concept application to a monitored rainfall–runoff event in the Valfredda catchment (northeastern Italy), which achieved reasonably good performance and illustrates the potential of the proposed framework in real‐world settings. The proposed framework bridges classical unit hydrograph theory and empirical evidence on channel network dynamics, providing a new pathway for representing catchment functioning in hydrological models.

Understanding changes in extreme precipitation is critical for enhancing climate resilience, particularly in regions such as West Africa, where communities are highly vulnerable to hydroclimatic extremes. This study examines the spatial and temporal variations in extreme precipitation across West Africa over 1998–2024 using high-resolution satellite data from NASA’s Integrated Multi-satellitE Retrievals for GPM (IMERG) V07B Final Run product. Extreme precipitation is analyzed using daily accumulated precipitation indices, including absolute and threshold-based indices. The Theil-Sen slope estimator and the Mann-Kendall test are applied to identify statistically significant trends at three significance levels. To assess the robustness of the detected trends, results from IMERG are compared with those from the Global Precipitation Climatology Project (GPCP) Version 3.3 Climate Data Record over the same period. The comparison shows generally similar spatial patterns and directions of change between the two datasets across West Africa. Results reveal distinct regional patterns of change. The Guineo-Congolian and Guinean regions, particularly Nigeria’s eastern coast, show significant declines in annual precipitation, along with significant decreases in the frequency and intensity of extreme events. In contrast, the western Sudanian and Sahelian regions, notably southern Mali and Burkina Faso, exhibit increasing trends in both total precipitation and extreme precipitation indices, indicating a shift toward wetter conditions driven by more frequent and intense extreme events. Furthermore, the contribution of extreme precipitation to annual totals varies across climatic zones, with smaller contribution from extreme days at lower latitudes. These findings highlight the importance of regionally tailored climate adaptation strategies for effective flood risk management and sustainable water resources.

Sub-daily quantitative precipitation forecasts (QPF) are a primary input to flood early warning, yet over Brazil they are hampered by convective, intermittent rainfall and a sparse observing network. We present a machine-learning post-processing system (V2.0C) producing 3-hourly QPF on a 0.25° grid by coupling a LightGBM precipitation-occurrence classifier with a Tweedie amount regressor and a dual-gate routing scheme with a physically guarded heavy-rain (≥5 mm) hard route. Its 71-dimensional feature set is dominated by GEFS ensemble statistics. The system is verified over two independent windows — April–May 2026 and the complete austral-summer convective season (October 2025– March 2026) — against the IMERG satellite product (areal) and the INMET automatic-station network (point). Against IMERG, V2.0C attains the highest correlation (0.324 and 0.286) and a heavy-rain CSI (≥10 mm) of 0.063–0.071, 3.3–4.4 times raw GFS, with stable month-to-month skill through the SACZ peak. A common mask analysis localizes the 60–65% ensemble-driven correlation gain to the southern third of the domain, where GEFS statistics are the only direct precipitation predictors available. Controls show that the guarded routing makes a high-recall heavy-rain expert safe to deploy: it adds 6–10% heavy-rain detection over a no-expert control, whereas unguarded use halves heavy-rain CSI. Gauge verification reproduces the heavy-rain advantage (CSI ≥10 mm an order of magnitude above raw GFS) and the wet bias, and the point–area correlation gap is explained by sub-grid representativeness. The detection-oriented bias is removable a posteriori: leakage-free quantile mapping eliminates it (bias +0.02 mm, MAE −69%) without reducing detection, supporting a dual-product strategy for flood-warning and hydrological applications.

The zenith total delay (ZTD) retrieved from 5G communication base stations represents a novel observation source for numerical weather prediction (NWP), leveraging the extensive global deployment of 5G infrastructure. In this study, the direct assimilation of 5G‐ZTD observations into a high‐resolution NWP system was accomplished for the first time. Two precipitation events were selected to evaluate the impact of 5G‐ZTD assimilation on short‐term precipitation forecasting. Assimilating 5G‐ZTD data corrected the dry bias in the initial water vapor field and improved precipitation forecasts in both cases, demonstrating its practical utility for short‐term heavy rainfall forecasts and operational potential given its low cost and extensive coverage.

Floods are among the most frequent and destructive natural disasters, yet future flood risk assessments have largely relied on precipitation indices, overlooking runoff depth—a variable that more directly captures flood hazard under diverse regional hydrological conditions. Moreover, the interactions among risk components (hazard, exposure, vulnerability) remain underexplored. Here we integrate 14 multidimensional sub‐indicators, including both precipitation‐ and runoff‐based indices, to assess global rainstorm flood risk from 1991 to 2100 under four SSP scenarios (SSP1‐2.6, SSP2‐4.5, SSP3‐7.0, SSP5‐8.5). Comparative experiments reveal that omitting runoff depth systematically overestimates high‐risk areas and brings forward the time at which risk projections begin to diverge across scenarios. Global risk increases persistently, with SSP5‐8.5 showing the highest rise by 2100, and high‐risk regions are concentrated in the Americas, Africa, South Asia, East Asia, and Southeast Asia. Exposure dominates risk, while hazard and vulnerability play regionally distinct modulating roles. Alarmingly, by 2100, half of the global population (up to 6 billion under SSP3‐7.0) and half of global GDP (under SSP5‐8.5) will face the highest risk level, with South and East Asia accounting for half of that population (or GDP) due to dense populations and infrastructure. This study advances flood risk assessment by reconciling hydrological complexity with socioeconomic pathways and highlights that mitigation strategies must prioritize emission reductions and the relocation of people and assets from flood‐prone areas.

This study develops a Superdroplet‐Bin hybrid microphysics scheme within the Weather Research and Forecasting (WRF) model to investigate hygroscopic seeding of warm convective clouds. The Super‐droplet method describes 3D transport, deliquescence, condensation, and collision‐coalescence growth of seeding agents or solution droplets, while the spectral bin method simulates liquid‐phase processes. Idealized simulations indicate that seeding full‐spectrum salt near cloud base can produce a maximum accumulated precipitation of 11.59 mm in 3 h. Further sensitivity experiments on seeding particle size reveal that rainfall enhancement primarily originates from particles ≥4 μm, with 5 μm yielding the most pronounced effect. This is because particles ≥4 μm can nucleate to form droplets ≥20 μm, shifting collision‐coalescence from the low‐efficiency regime below 20 μm to the high‐efficiency regime between large and drizzle‐sized droplets. The tail and rain‐embryo effects are identified as the dominant mechanisms, competition alone cannot effectively broaden the droplet spectrum or trigger precipitation.

The TV-SAT signals received by the ground antennas of Satellite Microwave Links (SMLs) can be opportunistically used for detecting and quantifying precipitation. Consequently, SMLs have the potential to serve as low-cost rainfall sensors complementing conventional instruments. However, a major challenge for opportunistic sensors, including both SMLs and their terrestrial counterpart – Commercial Microwave Links (CMLs) – is related to data ownership and accessibility constraints, possibly hindering progress in the development of processing tools and validation studies. This limitation highlights the importance of openly accessible datasets. While CML open datasets are already available, there are no large-scale SML datasets in public repositories. To address this gap, we introduce the OpenSat4Weather dataset, a comprehensive and openly accessible collection of measurements from 215 SML sensors deployed in Southern France, over a five-month period from August to December 2022. The dataset is accessible at https://doi.org/10.5281/zenodo.16530166 (Nebuloni et al., 2025b). OpenSat4Weather also includes concurrent conventional measurements, namely, 6 min rainfall depths from 113 operational rain gauges, and radar-based estimates of rainfall intensity along each SML path. The radar data were derived from the gauge-adjusted weather radar product PANTHERE provided by Météo-France. Additionally, the dataset stores ERA5 reanalysis data of the 0 °C isotherm height for rain height estimation, which are essential for accurately converting the received signal level into rainfall intensity. In this paper, we detail the OpenSat4Weather data preparation process and draw statistics of data availability. We then provide a descriptive analysis of the dataset, including rainfall characteristics, derived from rain gauges, statistical properties of the SML received signal levels, and a comparison between SML and radar data. In addition, we illustrate examples of disturbances and anomalous patterns observed in raw SML signals. Finally, we provide some practical guidelines for processing and analyzing SML data. Our ultimate goal is to promote open research initiatives aimed at accelerating the development of SML-based applications. Enhancing rainfall monitoring by opportunistic sensors has the potential to provide substantial benefits, particularly in regions where conventional…

Groundwater is a critical source for vegetation in drylands, yet the quantification of vegetation dependence on groundwater (VDG) remains underexplored. Understanding this dependence is essential for unraveling vegetation‐groundwater interactions and informing sustainable water resources management in drylands. This study constructed a vegetation response curve to depth to groundwater (DTG) by analyzing the relationship between DTG and the Normalized Difference Vegetation Index (NDVI) at the pixel scale. A new method was developed to quantify VDG from the fitted NDVI–DTG response curve, where VDG was defined as the rate of NDVI change per unit change in normalized DTG at the inflection point of the NDVI–DTG response curve. VDG values were obtained for nine representative arid, semi‐arid, and semi‐humid regions in northern China (with an annual precipitation gradient of 200–800 mm). VDG shows pronounced spatial variation across the nine regions, with mean values decreasing from approximately 1.28 in the driest region to approximately 0.24 in the wettest region along the precipitation gradient. A strong negative correlation between annual precipitation (P) and VDG was identified (VDG = 2.08e −0.0037P ), highlighting precipitation as a dominant regulator of VDG across regions. The research provides new empirical evidence for a deeper understanding of the effects of groundwater on vegetation, and also provides strong scientific support for groundwater management and ecological restoration with various annual precipitation amounts.

Water transit time (TT), the time elapsed from its entrance into a catchment and its exit through a stream section, is a useful indicator of catchment transport dynamics. Its distribution (TTD) is typically estimated at a single cross section where tracer data are available. However, spatial variability of TTD is key to understanding how heterogeneous source areas contribute to solute and water quality dynamics. Here, we investigate how TTD varies along a stream network. To this end, we analyzed stable isotopes in rainfall (three stations) and streamwater (eight sampling sites) in the Valfredda network draining an alpine catchment. To quantitatively analyze the data, we developed a spatially explicit model of TTD based on StorAge Selection (SAS) functions. The catchment is discretized into subcatchment units, each characterized by distinct SAS functions. Subcatchment TTDs are routed through the network to estimate TTDs at every cross section. Parameter estimation relies on a hierarchical Bayesian framework, which ensures consistency across subcatchments while allowing heterogeneity. Results indicate that streamflow preferentially samples older water from storage, with only a limited number of flow events triggering young water release. Despite pronounced heterogeneity in geomorphology and land cover, the inferred SAS functions exhibit only mild spatial variability. Differences in TTDs are driven primarily by variations in precipitation phase (liquid vs. solid) and the timing of snowmelt‐driven fluxes, rather than by intrinsic differences in mixing mechanisms. Both sources of variability are explicitly represented within the proposed framework, highlighting its general applicability to reveal transport dynamics at the catchment scale.

Monsoon rainy seasons shape water supply and flood hazard across northern East Asia, but their long-term variability remains poorly understood because few daily rainfall records extend beyond the instrumental era. Here, we reconstruct rainy-season onset, retreat, and duration from a 256-year daily precipitation record for Seoul (1770–2025), providing an unusually long view of seasonal monsoon behavior. The reconstruction reveals structured quasi-decadal to multidecadal variability in rainy-season duration, indicating that recent rainy-season lengthening over northern East Asia is consistent with the latest positive excursion of a recurrent multidecadal modulation rather than a purely isolated modern anomaly. Coupled with warming-associated intensification of extreme rainfall, this prolonged-duration phase has elevated the compound-hazard probability, with recent probability peaks exceeding the long-term mean of ∼25% in Seoul. Exploratory near-term outlooks suggest that rainy-season duration in northern East Asia is likely to remain prolonged toward mid-century, sustaining heightened compound hydroclimatic hazard when seasonally extended overlaps with intensified event-scale rainfall.

Global warming has intensified extreme precipitation, a trend projected to continue as warming persists. However, how extreme precipitation characteristics respond to global warming mitigation strategies remains poorly understood. This study examines East Asian summer extreme precipitation responses under a net-zero anthropogenic carbon dioxide (CO 2 ) emission scenario. Despite substantial cooling over East Asia following atmospheric CO 2 concentration decline, the intensity of extreme precipitation events continues to increase for about 30 years and does not subsequently weaken. This counterintuitive result arises mainly from intensified upward motion during extreme precipitation events, driven by enhanced baroclinicity and strengthening of the upper-tropospheric jet, which together overcompensate the cooling effect. These are essentially due to the hysteresis in the Atlantic (i.e., Atlantic Meridional Overturning Circulation slowdown), Southern (i.e., sustained warming), and Pacific (i.e., El Niño–like warming) Oceans. Our findings highlight that achieving net-zero anthropogenic CO 2 emissions is insufficient to mitigate intensifying extreme rainfall events in the densely populated East Asian countries.

This study presents an integrated framework for estimating Probable Maximum Precipitation (PMP)‐equivalent return levels under climate change, addressing two critical challenges: sampling uncertainty for rare events and the validation of bias correction under different global warming scenarios. Leveraging large regional climate model ensembles, the framework combines spatial transposition, based on L‐moment homogeneity, with ensemble pooling to expand the effective sample size to nearly 10,000 independent annual maxima per location. Results show that spatial transposition significantly reduces uncertainty in rare‐event return levels, narrowing 95% confidence intervals by over 60% for the 10,000‐year event. Improvements were most notable in the upper tail, where empirical quantiles closely matched Generalized Extreme Value fits due to increased parameter stability from the expanded sample size. Subsequently, three bias correction techniques, CDF‐transform (CDFt), Quantile Delta Mapping (QDM), and Quantile Mapping (QM), were applied to CanRCM4‐LE and evaluated against CRCM5‐LE using a pseudo‐observation framework that allowed assessment under different warming scenarios. CDFt consistently provided the best agreement with distributional properties and empirical quantiles, followed by QDM. Using the best‐performing bias correction method applied to CRCM5‐LE with ERA5 reanalysis serving as the observational reference, PMP‐equivalent return levels at grid cells corresponding to 2,730 dam locations across Northeastern North America, show spatially coherent increases, with median 100,000‐year return levels rising by ∼57% under +1.5°C and ∼77% under +3.0°C warming. Broader spatial patterns mirror this intensification, underscoring the thermodynamic drivers of future extremes. The framework enables robust, reproducible PMP estimation and supports adaptive design strategies for critical infrastructure under climate uncertainty.

This study analyzes how tropical cyclone (TC) precipitation in the inner core (IC) and outer region (OR) varies with translation speed and storm intensity using satellite‐derived precipitation data. Inner core precipitation initially increases with translation speed but decreases at higher speeds, while OR precipitation increases nearly linearly with translation speed. Inner core precipitation is strongly linked to TC intensity, both with and without conditioning on translation speed, whereas OR precipitation increases with intensity primarily for stronger storms. As translation speed increases, the location of maximum precipitation shifts toward the forward‐left quadrant in the Northern Hemisphere, accompanied by a pronounced increase in rainfall magnitude. These results suggest that IC precipitation is primarily governed by storm dynamics, whereas OR precipitation is more sensitive to environmental forcing.

Accurate representation of raindrop size distribution (DSD) is crucial for understanding precipitation microphysics and improving quantitative precipitation forecasting. Conventional methods in numerical models often prescribe the DSD shape parameter as a constant or derive it from empirical relationships based solely on disdrometer observations, limiting their ability to capture DSD variability driven by dynamic, thermodynamic, and microphysical processes. This study develops a machine learning (ML) framework integrating disdrometer observations with environmental atmospheric variables to retrieve DSD parameters. Trained on data from 11 stations in Shanghai during 2019–2021, three ML models outperform conventional methods, effectively reducing biases and capturing observed DSD variability. They remain robust in independent heavy‐rainfall and tropical cyclone validations during 2022–2025, providing reliable DSD and rain rate estimates. Feature analysis and ablation experiments further underscore the critical role of environmental factors in improving retrieval accuracy, offering a data‐driven pathway to improve DSD parameterizations in numerical models.

Great Britain's variable maritime climate has until relatively recently limited the utility of seasonal hydrological forecasts. The latest generations of seasonal atmospheric forecasting systems have created new opportunities to improve flow forecasting across Great Britain, such as for the UK Hydrological Outlook. Here, we combine newly-developed high-resolution rainfall forecasts derived from historical weather analogues (HWA) conditioned on large-scale circulation patterns with a monthly-resolution national-scale hydrological model to produce seasonal and monthly forecasts. This forecasting scheme is evaluated using hindcasts from 1993–2017. We show that the skill of the rainfall forecasts is comparable to that achieved at coarser resolutions by other models. However, increasing the spatial resolution has degraded some of the skill found by previous authors producing HWA forecasts at lower resolutions. The river flow forecasts perform better than rainfall, with much of the improvement attributable to the skill provided by hydrological initial conditions. Both rainfall and river flow forecasts are most skilful in winter and perform adequately in spring and autumn. We show that in responsive catchments, primarily found in the northwest, rainfall forecasts contribute most of the skill in the river flow predictions but are much less important in regions with long hydrological memory. We thus indicate where flow forecasts would benefit most from improved rainfall forecasts or from better hydrological modelling. The introduction of these high-resolution rainfall forecasts now enables hydrological forecasting at unprecedented levels of detail across Great Britain and demonstrates a forecasting approach that may be similarly beneficial elsewhere in the world.

Streamflow reanalysis is vital for water resources management and climate impact assessment; however, the extent to which it is affected by precipitation forcing errors remains poorly understood. Focusing on the Global Flood Awareness System driven by the European Centre for Medium-Range Weather Forecasts Reanalysis v5 (GloFAS-ERA5), this paper details how streamflow reanalysis errors respond to precipitation errors. Specifically, the root mean square errors (RMSEs) are calculated by hydrological year for reanalysis products across 671 catchments in the Catchment Attributes and Meteorology for Large-sample Studies (CAMELS) dataset; and by combining catchment-specific linear regression with global panel regression, the effects of precipitation errors on streamflow errors are quantified. The results demonstrate an improved performance from GloFAS-ERA5 v2.1 to v4.0, with the median RMSE decreasing from 2.16 to 1.81 mm. For GloFAS-ERA5 v4.0, the panel regression estimates an average increase of 0.51 mm in streamflow RMSE for each 1 mm increase in precipitation RMSE across the 671 catchments. In the meantime, the corresponding catchment-specific increase of streamflow RMSE reaches up to 2.5 mm in humid catchments but remains below 0.7 mm in arid catchments. These divergent responses suggest that streamflow errors may be more directly associated with precipitation errors under the saturation-excess conditions while soil moisture deficits can dampen their effects. Furthermore, incorporating interaction terms into panel regression increases the coefficient of determination ( R 2 ) from 0.16 to 0.36, indicating these responses are modulated by catchment heterogeneity. This modulation is further presented by targeted case studies, indicating that temperature controls the storage and release of snow water, thereby dampening and delaying the responses of streamflow errors to precipitation errors in snow-dominated catchments. These findings provide a valuable diagnostic method and practical guidance for applications of global streamflow reanalysis to complex, heterogeneous catchments.

In May 2019, unprecedented extreme rainfall over the central United States (US) facilitated severe flooding along the Arkansas River. The well‐documented relationship between atmospheric rivers (ARs) and central US heavy precipitation motivated investigation into AR relevance to this event and its predictability on extended timescales. AR conditions coinciding with the extreme precipitation indicate ARs contributed to the Arkansas River flooding, while poisson weighted forecast probabilities of SHiELD ensemble hindcasts show both the ARs and extreme precipitation were predictable at 2 week leads. Comparison of precursory atmospheric conditions between the top and bottom five performing ensemble members reveal the top performers developed a Rossby wave train over the North Pacific, which promoted AR‐favorable conditions downstream and facilitated better forecasts of precipitation than the bottom performers. Results suggest cross‐Pacific Rossby wave trains can provide a forecast of opportunity for extended predictability of ARs and extreme precipitation over the central US.

Global warming is reshaping precipitation extremes and increasing temporal rainfall unevenness, yet precipitation unevenness has rarely been quantified at the scale of consecutive precipitation events. Using daily observations from 19,183 gauge stations worldwide during 1980–2022, we defined WE50 as the total duration of the consecutive wettest precipitation events required to deliver 50% of annual precipitation, and WE50_event as the number of selected events contributing to that half. The global mean WE50 is 27.8 days, and WE50_event is 9.4 events, with Europe exhibiting the largest values. Trend analyses show that WE50 decreases significantly at 45.7% of global stations, whereas WE50_event changes are generally weak. A decomposition further indicates that the global WE50 decline is largely attributable to shorter durations of consecutive precipitation events, with comparable contributions observed in Europe and South America, whereas regional differences emerge in the contributions of precipitation amount, event intensity, and duration.

Drought is one of the most severe and complex natural hazards causing changes in water balance and impeding crucial water‐dependent ecosystem services. Over the last 25 years, multiple severe droughts linked to El Niño events have affected the Zambian and Zimbabwean regions. As a result, Lake Kariba, world's largest artificial reservoir by volume, saw historically low water levels and power shortages from reduced hydropower generation. This study employs a novel integrated approach to investigate the effects of varying water levels on the spatio‐temporal dynamics of phytoplankton blooms across the lake, combining remote sensing, in situ measurements, and a three‐dimensional hydrodynamic model (Delft3D). Satellite Earth Observations of lake water level, surface water temperature, and Chlorophyll‐a concentration are used, and spatially resolved water transparency is estimated from lake water leaving reflectance. The Delft3D model is calibrated and validated against in situ and satellite data, including Rainfall Estimates from Rain Gauge and Satellite Observations (CHIRPS), and in situ discharge data, while assimilating maps of water transparency to simulate surface thermal stratification accurately. Results show pronounced spatial and seasonal heterogeneity in transparency, thermal structure and phytoplankton dynamics across the three basins. Phytoplankton phenology varies from short, pulse‐like blooms in the most sensitive riverine upstream basin to longer, more coherent bloom seasons in downstream transitional and lacustrine basins. Bloom hotspots occur near shorelines and tributary mouths during windy and wet seasons, following basin‐scale circulation patterns and possibly nutrient inflows. Our integrated approach captures Lake Kariba's heterogeneity and identifies vulnerable areas, highlighting its importance for effective water resource management.

Atmospheric rivers (ARs) are key drivers of extreme precipitation, and their future changes indicate major climate risks. Historical variations of ARs in Asia exhibited dynamic suppressions and thermodynamic enhancements under climate change, yet critical gaps remain in understanding the future impacts of these competing mechanisms. Based on a framework integrating the largest ensemble of multiple high-resolution global climate models for AR projection with multiple AR detection configurations, the projected changes reveal a dynamic inhibition of near-future ARs dominating lower latitudes under warming, while thermodynamic enhancement dominates densely populated subtropical and midlatitude regions. This pattern relates to the anomalously steered moisture transport by the westward-extended subtropical high. Consequently, population exposure to AR-induced extreme precipitation is projected to increase across 58% of terrestrial AR-active zones, particularly in urban regions within northern China, India, and Bangladesh (+24–59%). These findings underscore the urgency of implementing adaptation strategies against AR hazards in these regions during the coming decades.

Precipitation seasonality is commonly described using wet and dry periods, but this framework can obscure multiple rainfall maxima within a rainy season. Here we introduce a global framework for identifying the number of precipitation peaks (NPP) during rainy periods from daily precipitation using optimal harmonic seasonal fits and reproducible peak‐selection criteria. We find that multimodal precipitation seasonality is widespread, covering approximately 60% of global land. Most multimodal regions have two or three peaks, whereas higher‐order regimes occur mainly in weakly seasonal dry or wet regions, where the seasonal cycle is poorly defined and more sensitive to precipitation variability. Detected peaks are generally separated by seasonal‐scale intervals. Temporal evolution of NPP demonstrates weak long‐term trends, with larger temporal variability concentrated in regions with relatively weak seasonality. These results provide a systematic global characterization of precipitation peak structure and establish NPP as a complementary metric for diagnosing precipitation seasonality.

The Canadian Surface Reanalysis (CaSR) includes an offline, high-resolution gridded total precipitation reanalysis designed to provide accurate estimates across North America. This product, referred to as CaPA-24h, builds on the Canadian Precipitation Analysis (CaPA) system of Environment and Climate Change Canada (ECCC). It combines a dense network of daily surface observations with a background field from the CaSR dynamical component, using updated quality-control and assimilation procedures to filter spurious observations. This study evaluates the CaPA-24h fields produced in CaSR v3.2, together with their background field, and compares them with the previous version (v2.1) as well as with two independent datasets, ERA5-Land and PRISM. Results show substantial improvements in v3.2, particularly in data-sparse regions, with an enhanced representation of precipitation events of different intensities. Compared to ERA5-Land, CaPA-24h v3.2 provides more accurate seasonal and regional precipitation patterns, while evaluations against PRISM confirm this improved performance. However, biases persist in southern and western mountainous areas, especially for orographic precipitation. A first-time assessment of the hourly disaggregated product reveals limitations in the diurnal cycle representation, indicating the need for refined disaggregation methods and background field generation. Overall, CaPA-24h v3.2 delivers a reliable and well-established gridded precipitation dataset, offering a valuable resource for hydrological, climatological, and impact studies across North America.

Near‐surface groundwater dynamics (NSGD) regulate water residence time and downstream water resources in mountain watersheds, yet whether a single extreme rainfall event can reorganize near‐surface storage remains unclear. Here, we examine typhoon‐driven NSGD using ambient seismic noise from four stations, together with hydrological and meteorological records. Single‐station cross‐component correlations and the stretching method were applied to estimate seismic velocity changes ( dυ / υ ) at 4–8 Hz. Following the typhoon, has spatial variability, ranging from ∼10 days upstream to at least 3 months downstream. We attribute this delayed recovery to thicker colluvium and fractured bedrock that enhance subsurface water retention. Concurrent decreases in dυ / υ and increases in groundwater levels indicate transient vertical coupling between shallow and deeper systems. Continued post‐typhoon dυ / υ decreases suggest delayed lateral inflow from upstream, whereas later low‐intensity rainfall responses indicate subsequent decoupling. Our results show that a single typhoon prolonged watershed scale NSGD with insight for water residence time in mountainous regions.

Monitoring continental runoff is important for the global water cycle. Here, we update downstream monthly streamflow for the world’s 936 largest rivers to 2024 using multiple data sources for quantifying variations and long-term changes in continental discharge from 1950 to 2024. The update added new data for 697 rivers and extended the record length for 512 rivers. Still, many large rivers in South and East Asia, Africa, and some in South America have short records, and stations with observations have decreased since 1978. Streamflow in the world’s major rivers exhibits large interannual-to-decadal variations that are correlated with basin-mean precipitation, with insignificant long-term trends since 1948 for most of the rivers. Out of the 200 rivers with the largest discharges, only 71 (35.5%) show statistically significant streamflow trends during 1948–2024, with 34 being positive and 37 being negative. Global continental discharge decreased slightly from 1949 to 1993 and then rebounded to levels slightly above the 1950–90 mean in recent years. The streamflow-inferred runoff trends during 1950–2024 show coherent spatial patterns that are broadly consistent with observed precipitation trends, both showing increases over the central and Northeast United States, northern Europe and most of Russia, Argentina and Uruguay, and northwestern Australia, but decreases over most of Africa, East and South Asia, eastern Australia, the western United States, western and eastern Canada, and parts of Brazil. Both internal climate variability (especially from the tropical Pacific) and externally forced changes [e.g., due to carbon dioxide (CO 2 ) increases] likely contributed to these regional trends in precipitation and runoff, with below (above)-normal land precipitation and runoff during warm (cold) phases of the tropical Pacific sea surface temperatures.

Atmospheric rivers (ARs) drive winter hydroclimate extremes over the Iberian Peninsula (IP). This study evaluates whether exceptional landfalling ARs (LARs) between October 2025 and February 2026 displayed characteristics expected under early high‐emission forcing. High‐resolution WRF simulations forced by ERA5 and CESM2 under SSP5‐8.5 were used, using IPART for AR identification and FLEXPART‐WRF for moisture tracking. The simulated LARs reached vertically integrated water vapor transport magnitudes matching, and locally exceeding, those under the early SSP5‐8.5 climate response by 10%–15%. Mid‐tropospheric trough–ridge patterns enhanced zonal flow and long‐range moisture advection. Subtropical Atlantic moisture sources were 5%–20% stronger than simulated under the early SSP5‐8.5 response. LAR‐related precipitation over Atlantic‐facing IP sectors also matched or significantly exceeded ( p < 0.05) early SSP5‐8.5 levels. These results suggest that present‐day extreme ARs can generate moisture transport and hydrological impacts comparable to early transient high‐emission projections.

Dynamic imbalances in model initialization severely constrain the predictability of the rapid intensification (RI) and abrupt track recurvature of tropical cyclones (TCs). In this study, the effectiveness of an incremental analysis update (IAU)‐based four‐dimensional variational (4DVar) data assimilation scheme in forecasting Typhoons Doksuri and Khanun in 2023 was investigated. By reducing spin‐up impact, the mean track errors obtained with the IAU scheme stabilized below 60 km, and the intensity forecast errors were consistently reduced compared to the control experiment. These improvements can be mechanistically attributed to a superior representation of the fractured subtropical high and the internal asymmetric dynamic structure of TCs, which resulted in critical steering flow correction. Consequently, this dynamical enhancement yielded a 60% relative improvement in forecast skill for heavy rainfall (≥50 mm) at the 72‐hr lead time, indicating the potential benefit of improved TC initialization for heavy precipitation prediction.

Atmospheric Rivers (ARs) often co‐occur with other weather systems, including fronts, low‐pressure systems (LPSs), and mesoscale convective systems (MCSs), which can amplify precipitation and flood risk. While extreme rainfall is frequently linked to multiple interacting features, how these systems co‐occur with AR intensity scales and regions remains unclear. Using ERA5‐derived feature masks and satellite data, we quantify spatiotemporal overlaps between ARs and other systems. AR + Front combinations dominate across much of the conterminous U.S. In higher AR scales (4–5), more complex MCS‐related combinations become increasingly common, particularly in DJF and MAM and along the West Coast, while limited LPS‐related co‐occurrences appear mainly in JJA and SON and are confined to regions such as the Gulf of Mexico and offshore Pacific genesis regions. In contrast, AR‐only occurrences dominate in some inland western regions. Extreme precipitation exhibits seasonal variation, with distinct co‐occurring systems contributing across seasons.

Stationary precipitation measurements are frequently affected by undercatch errors, which are particularly pronounced in cold and alpine regions with strong winds. Since gridded precipitation products used in land surface modeling are often derived from spatial interpolation of meteorological station data, these measurement errors propagate directly into gridded datasets. In this study, we train monthly Generalized Additive Models (GAMs) using undercatch corrected station observations, with geographical exposure and terrain elevation as predictors, achieving R 2 values above 0.76 in Leave-One-Out Cross-Validation. We apply these models to generate monthly undercatch correction factors for Austria and – combined with an exposed terrain penalty – use them to adjust existing station-based gridded precipitation products. We validate the undercatch correction using the conceptual rainfall-runoff model COSERO across Austria and in two high-alpine reservoir catchments: Kölnbrein and Schlegeis. Our results demonstrate that retrospectively corrected precipitation reduces runoff simulation biases across Austria, especially in catchments above 1500 m elevation, and closes the water balance in both alpine study regions where uncorrected data showed runoff deficits exceeding 20 %. Biases in snow depth simulations – assessed using the physically-based snowpack model Alpine3D and validated against stereo-satellite observations – decrease from a median difference of −0.87 to +0.15 m. Additionally, undercatch-corrected precipitation enables more realistic simulations of snow covered area during the melting season and long-term glacier volume changes. The proposed method shows promising results in both alpine case study catchments and across Austria, highlighting the importance of accounting for undercatch errors in high-alpine terrain and indicating the need for further research into their magnitude at high elevations.

The lack of reliable, spatiotemporally-completed and millennium-long drought dataset for China has limited a full-field and accurate understanding of drought variability over the past millennium. Here, we present a millennium-long gridded dataset of summer (June-August) Standardized Precipitation Evapotranspiration Index (SPEI) for China at 1.875° spatial resolution and annual temporal resolution. This dataset was generated using the paleoclimate data assimilation (PDA) approach by assimilating an expanded proxy network comprising 186 tree rings, 69 historical documents, three stalagmites and two ice cores from China and adjacent regions - addressing the limitations of previous gridded drought reconstructions that lacked proxies in eastern China and had sparsely-distributed proxies in northwestern China. Validation indicated that this dataset exhibits substantially-improved reliability compared to model-only calculations and previous PDA-based reconstruction. As a significantly-improved, millennium-long gridded SPEI dataset for China, this product provides essential support for elucidating the spatiotemporal evolution of drought variability over the past millennium, contextualizing current droughts within that period and evaluating climate model performance in simulating long-term drought events.

Climate change is intensifying the global water cycle, with extreme precipitation events increasing in frequency and intensity at the global scale. While trends in daily precipitation extremes are well-documented, sub-daily extremes – critical for flash flood risk – remain poorly characterized, due to limited long-term sub-daily observations. Convection-permitting models explicitly resolve deep convection and therefore offer the potential for a substantially improved representation of convective processes and short-duration precipitation extremes. This study evaluates the ability of the convection-permitting regional climate model AROME (2.5 km resolution, 1959–2022), forced by ERA5 reanalysis, to reproduce precipitation extremes and their trends, at daily and hourly scales, using a dense network of Météo-France stations. Using extreme value theory (GEV modeling), we analyze trends in 10-year return levels for both daily (1959–2022) and hourly (1990–2022) extremes. At the daily scale, AROME reproduces observed positive trends in southeastern France, consistent with previous studies. Hourly trends are more heterogeneous and less robust, with high spatial variability and low model-observation correlation. Overall, the results demonstrate the value of the explicit-convection model for studying extreme precipitation, while also highlighting its limitations for convective extremes.

Rapid, shallow landslides in coastal mountains are triggered by extreme precipitation, shaping topography and impacting human settlements. Using an inventory of >700 landslides mapped from satellite imagery (2009–2024) and an atmospheric river database (1981–2019), this study documents linkages between climatic drivers and the topographic context of landslides on Prince of Wales Island, Southeast Alaska. We observe a strong correlation between landslide occurrence and extreme atmospheric rivers during the autumn months. Notably, landslide initiation zones exhibit a strong directional bias toward the southwest‐to‐southeast, coinciding with the trajectory of landfalling extreme atmospheric rivers. Our frequency ratio analysis demonstrates that landslides are overrepresented on the windward aspect of steep (>35°) slopes at mid‐slope positions which enables us to map relative landslide susceptibility. Potential mechanisms include orographic forcing, wind‐driven precipitation, and forest canopy disturbance. These findings provide a framework for quantifying how slide‐prone landscapes co‐evolve with preferential climate forcing to inform hazard assessment.

Atmospheric rivers (ARs) are essential yet hazardous components of the global water cycle. Despite the development of multiple classification frameworks, their interconnections remain unexplored. Here, we present the first global quantification of the statistical linkages among AR classifications based on oscillation frequency, moisture‐wind contributions, and association with extratropical cyclones. These findings imply a unified “wind‐driven, cyclone‐associated” archetype, marked by significant convergence among high‐frequency, windy, and cyclone‐related ARs. Non‐cyclone‐related ARs represent a broad residual category with mixed characteristics. Composite lifecycle analysis further shows that wind contribution generally increases during AR evolution, while oscillation frequency remains relatively stable. This integrated framework bridges fragmented understandings of AR diversity, providing a consolidated reference for forecasting and climate modeling.

Uganda’s economy hinges on sectors with the highest sensitivity to rainfall variability. The lack of a comprehensive understanding of the nature and dynamics of rainfall seasons has thus profoundly impacted it. We modify and apply a dual-phase seasonal search scheme to characterize rainfall seasonal performance over Uganda from 1990 to 2024, using a state-of-the-art Climate Hazards Group Infrared Precipitation with Stations (CHIRPS), version 3 (CHIRPS3). We apply novel validation, anomaly, trend, mean difference, hotspot, and regression analyses in a spatiotemporal context. Results reveal superior performance of CHIRPS3 against ground observations on both grounds of magnitude, seasonal cycle, trend, and variance. Rainfall seasonality was characterized by pronounced spatial gradients with higher rainfall magnitudes and longer seasonal durations over the Lake Victoria basin (LVB) and western highlands but persistently shorter and drier seasons in northeastern Uganda. The first rainfall season (S1) typically begins earlier (late February) in southern and western regions and later in the north, while the second rainfall season (S2) generally exhibits earlier onset (early August) and later cessation across northern and western Uganda. Trend analysis indicates a widespread shortening (∼2 days per season) and weakening (∼15 mm per season) of the S1 season, contrasted by a lengthening (∼3 days per season) and intensification (∼20 mm per season) of S2 rainfall, particularly in northern and eastern regions. Both seasons were variedly characterized by above-normal and below-normal episodes with occurrence probabilities in the range of 5%–35%. Interseasonal comparisons further demonstrate S2’s dominant contribution especially over northern and western Uganda, whereas S1 remains dominant in the LVB. Finally, El Niño–Southern Oscillation (ENSO) and Indian Ocean dipoles (IODs) had strong but varied influences on seasonal nonexceedances and exceedances. The findings indicate a shift in Uganda’s seasonal rainfall structure with important implications for agricultural planning, water resource management, and climate adaptation strategies. Thus, we provide a comprehensive and high-resolution assessment of Uganda’s rainfall seasonality using updated CHIRPS3 data, offering crucial insights for climate resilience planning and sustainable development.

Precipitation estimates are subject to substantial errors, but their propagation into streamflow simulations remains an open question. This study uses a combinatorial approach to investigate this problem. First, the Raven hydrological modeling framework is used to identify behavioral model configurations by accounting for both parameter and structural uncertainties. Second, unlike conventional approaches using synthetic perturbations, we use empirically derived precipitation differences from 405 observed–estimated satellite and reanalysis-based pairs and use them as estimates of precipitation error. Third, we characterize the relationship between precipitation input errors and resulting streamflow errors ( P – Q error relationship) using two methods: the random forest model and precipitation elasticity analysis. Finally, streamflow uncertainty is partitioned using Shannon entropy and Sobol indices. The methodology is applied to 16 Canadian Prairie catchments; the model is calibrated over 2008–23 to obtain behavioral parameter sets, which are then used to simulate streamflow under 405 precipitation estimates for 2018–23. Results show that precipitation-induced streamflow errors depend strongly on model parameterization, particularly as input errors increase. Behavioral parameter sets do not exhibit uniform error propagation despite similar calibration performance, highlighting the critical role of model configuration in shaping the P – Q error relationship. This relationship is captured through precipitation elasticity, with higher elasticity leading to greater error amplification. Furthermore, Shannon entropy decomposition reveals substantial precipitation–parameter interactions that are not fully captured by the Sobol method. Overall, our study shows that precipitation uncertainty strongly influences streamflow uncertainty in the Canadian Prairies, while commonly used performance metrics [Kling–Gupta efficiency (KGE), Nash–Sutcliffe efficiency (NSE), etc.] do not necessarily indicate model robustness to input errors.

Precipitation observations are critical in studying the impacts of climate change, but the implications of changes in measurement devices on data quality have not been systematically investigated. This study compares hourly precipitation observations from NOAA’s Local Climatological Dataset (LCD) to contemporaneous daily observations included in its Cooperative Observer Program (COOP). Over the years, LCD data was collected using a variety of different measurement devices, while the COOP data referenced in this study utilized just one: the 8-inch non-recording standard rain gauge (SRG). The study focuses on precipitation between 1975 and 2024 in the Northeast (NE) and Southeast (SE) regions of the United States. Short-term and long-term delta change factors were computed and evidence for temporal changepoints in the datasets was gathered. Compared to the daily COOP data collected in the same region, the hourly LCD observations in both the NE and SE display temporal variability likely associated with changes in instrumentation with the largest DCF reaching 12.56% for LCD and −2.76% for COOP stations. Variability in the LCD data is greater in the coastal subregion compared to the relatively consistent changes in the COOP data, especially over the earlier portion of the observation period, before most LCD stations adopted the All-Weather Accumulation Precipitation Gauge (AWPAG). Changepoints detected in both datasets were categorized geographically and were dissimilar in most cases. This study underscores the need for precipitation data homogenization and consideration of the potential ramifications of changes in instrumentation in evaluation of long-term precipitation trends.

Access to high-resolution long-term earth system projections is essential for advancing research in hydrology, agriculture, disaster management, and adaptation planning. This Data Descriptor presents an Artificial Intelligence (AI)-refined ensemble of Earth system model (ESM) projections for the conterminous United States at 1/24° (~4 km) spatial resolution. The dataset was generated by downscaling ten Coupled Models Intercomparison Project phase 6 (CMIP6) Earth system models (ESMs) under two emissions scenarios (SSP245 and SSP585) for an 80-year period (1980–2059). Two AI-driven methods, Super-Resolution Convolutional Neural Networks (SRCNN) and Super-Resolution Generative Adversarial Networks (SRGAN), were applied to produce high-resolution daily precipitation and minimum/maximum temperature. This descriptor documents the input datasets, processing workflow, bias-correction steps, file structure, variables, spatial and temporal coverage, and technical validation of the released data. Validation analyses compare the generated products with Daymet observations and existing downscaled datasets to characterize spatial patterns, biases, temporal consistency, and differences among products. The dataset provides daily high-resolution historical and future earth system projections that can support regional, impact assessment, and related applications, and details the AI downscaling framework, training protocols, and evaluation strategies to ensure reproducibility and usability.

High-resolution, reliable surface meteorological estimates are essential for hydrologic modeling, climate research, and disaster management. Previous phases of the North American Land Data Assimilation System (NLDAS-1 and NLDAS-2), developed jointly by NOAA, NASA, and academic partners, established widely used observation-driven land hydrology modeling environments over the contiguous United States, supporting applications such as drought monitoring, water resource management, agricultural planning, and land–atmosphere research. However, increasing scientific and operational demands require finer spatial resolution, broader geographic coverage, and near-real-time availability. Building on this foundation, we present the North American Land Data Assimilation System phase 3 (NLDAS-3), which expands coverage to all of North and Central America and increases spatial resolution to 1 km. We developed a 1-km, hourly gridded surface meteorological dataset available in both retrospective and near-real-time modes. NLDAS-3 integrates satellite-based precipitation products and reanalysis fields from NASA and partner agencies. Meteorological variables are downscaled using elevation-based corrections to better represent fine-scale spatial variability across complex terrain. Evaluation against more than 3000 ground stations and 10 widely used reference datasets demonstrates that NLDAS-3 consistently outperforms existing products. Relative to NLDAS-2, NLDAS-3 achieves substantial average reductions in root-mean-square error (RMSE), including 18% for precipitation, 10% for air temperature, 42% for surface pressure, and 22% for longwave radiation. Extended triple collocation analysis further corroborates these improvements. Overall, NLDAS-3 represents a significant advancement in the quality, consistency, availability, and spatial resolution of surface meteorological data, providing a valuable resource for research and operational applications across North and Central America.

Understanding how meteorological drought propagates to hydrological drought under natural conditions is essential for improving drought early warning, yet the roles of precipitation frequency and intensity in regulating this process remain poorly understood. Here, we quantified drought propagation time (DPT) and drought response rate (DRR) across mainland China under natural conditions, and further assessed the spatial effects of precipitation intensity and frequency using geographically weighted regression (GWR) and future climate projections. The results show that: (a) DRR declines significantly with aridity, with 22.9% of the study area showing DRR values of ≤0.2, indicating weaker meteorological–hydrological coupling in drylands. (b) GWR results indicate that meteorological controls on DPT are dominated by precipitation, while the effects of precipitation intensity and frequency exhibit strong spatial heterogeneity: total precipitation metrics fluctuate around zero across aridity classes, whereas light‐precipitation intensity and heavy‐precipitation frequency are more strongly associated with DPT in drylands, indicating that drought propagation depends more on precipitation partitioning than on bulk precipitation alone. (c) Under future climate scenarios (2030–2100; SSP2‐4.5 and SSP5‐8.5), future changes in drought propagation are driven less by shifts in mean precipitation intensity than by increasing uncertainty in precipitation‐frequency effects, especially in arid and hyper‐arid regions. These findings highlight the critical role of precipitation partitioning in regulating drought propagation and establish a natural‐condition baseline for drought early warning and climate adaptation in water‐limited regions.

Tropical cyclone (TC)‐induced extreme precipitation (TCEP) poses heavy risks, yet its seasonal predictability remains limited by insufficient understanding of cross‐scale climatic drivers. Here, we elucidate a stratosphere‐to‐stratosphere coupling pathway where the quasi‐biennial oscillation (QBO) modulates TC activity and subsequent TCEP over the southeastern coast of China, with the second boreal summer intraseasonal oscillation mode (BSISO2) acting as a critical mediator. The westerly QBO phase reshapes the spatial structure of BSISO2 during its Phases 4–6, creating favorable dynamical conditions and moisture convergence that nearly double TCEP over the southeastern China coast relative to the easterly QBO phase. Statistical fitting results further show that incorporating the QBO signal substantially improves seasonal TCEP predictability beyond conventional El Niño‐Southern Oscillation predictors. These findings establish the QBO as a coherent stratospheric driver for TCEP, providing a mechanistic basis for enhanced seasonal TCEP prediction and regional disaster preparedness.

The rainforests of South America and Africa are considerably reliant on rainfall supplied by evaporation from the rainforests themselves through moisture recycling. Thus, rainforest loss in these areas can reduce evaporation as well as subsequent rainfall in the wider forested region. During dry periods, a dampened water cycle may potentially aggravate rainforest water stress. However, few studies have investigated dry‐period moisture recycling statistics within rainforests over longer records and consistently compared the rainforest moisture recycling in both continents. Here, we analyze dry‐season anomalies of moisture recycling for mean‐years and dry‐years, in the South‐American (Amazon) and African (Congo) rainforests over the years 1980–2023. We find that dry‐season rainfall reliance on rainforest moisture increases slightly, but statistically significantly, by 5% (from a mean annual of 21% to 22%) in the Amazon and up to 12% (from 24% to 27%) in the Congo. At the same time, a lower fraction of rainforest evaporation stays within the rainforest during the dry season: 21% less from the Amazon (from 41% to 32%) and 22% less from the Congo rainforest (from 32% to 25%), which further decreases with increasing dryness. In response, areas downwind of the rainforests gain a higher dependency on rainforest moisture during dry seasons, amplified during dry years. For example, rainforest‐sourced rainfall in Ecuador increases from annually to in the dry season. The findings highlight the critical role of rainforests in maintaining rainfall during periods when water is already scarce.

The resistance and resilience of vegetation determine its ability to maintain ecosystem functional stability under flash drought stress. However, a research framework that quantifies event‐scale response characteristics of vegetation resistance, resilience, and vulnerability remains lacking. To fill this gap, we developed a high‐temporal‐resolution framework to assess the multi‐dimensional attributes of vegetation response to flash droughts, quantifying the spatiotemporal response characteristics of vegetation resistance, resilience, and vulnerability in China from 1982 to 2019. Our analysis shows that, over the past four decades, vegetation vulnerability to flash droughts in China's monsoon regions has increased by 0.019 per year. On one hand, this is primarily due to the significantly higher average decline rate of vegetation ecosystem resistance (0.074 per year) under flash drought stress, compared to the average increase rate of its resilience (0.010 per year). On the other hand, the trade‐off relationship between vegetation ecosystem resistance and resilience has shifted, particularly in the middle and lower Yangtze River Basin. In both monsoon and non‐monsoon regions, explainable machine learning models indicate that temperature is the most significant stress factor influencing vegetation resistance and recovery processes during flash drought events, with SHAP values contributing between 20% and 50% to the overall model explanation. These findings highlight the urgent need to focus on the impact of increasingly frequent flash droughts on the stability of terrestrial ecosystem carbon sink functions in the context of current global warming.

Seasonal snowfall is declining globally, yet the relative roles of precipitation variability versus warming‐induced phase transitions remain poorly quantified. We develop a physically based attribution framework that decomposes snowfall trends (1983–2023) into hydrological (precipitation‐driven) and thermodynamic (phase‐driven) components, constrained by a wet‐bulb‐temperature‐based phase relationship. Across most seasonal snow regions, snowfall decline is primarily driven by thermodynamic phase shifts rather than changes in precipitation amount. Regions near the rain–snow transition exhibit the strongest sensitivity to warming, consistent with systematic snowline retreat. Results are robust across independent data sets, including ERA5 reanalysis and the satellite‐based GPCP v3.3 products. These findings indicate that precipitation‐phase transitions exert pivotal control on contemporary snowfall loss, suggesting that projected precipitation increases are unlikely to offset warming‐driven reductions in snowfall. This has critical implications for future hydrological regimes and cryosphere‐climate feedbacks.

Vegetation restoration on the Loess Plateau has raised an important question of whether enhanced land–atmosphere coupling can alleviate regional water scarcity in this water-limited environment. In this study, we integrate the WAM-2layers atmospheric moisture-tracking model with a random forest–enhanced Budyko framework and introduced a vegetation-weighted leaf area index (LAIw) to examine vegetation-associated precipitation recycling and its hydrological effects during the peak growing season (July–August during 2000–2022). Results show that precipitation is dominated by terrestrial moisture sources (86.5 %), while oceanic contributions account for 13.5 %. Internal moisture recycling contributes 14.4 % of total precipitation but exhibits a trend of −0.073 mm yr−1. At the regional mean scale, vegetation-associated recycled precipitation has a limited effect on precipitation, evapotranspiration, and surface water availability (0.059 %, 0.02 %, and 0.107 %, respectively), reflecting strong compensation between positive and negative vegetation effects and the limited fraction of internal recycling. However, a clear regime shift emerges along the LAIw gradient, from weakly positive effects under low vegetation density to increasingly negative effects under high vegetation density. These findings suggest that under water-limited conditions, enhanced vegetation–atmosphere coupling does not necessarily increase surface water availability, but instead reflects a constrained redistribution of water within a monsoon–continental transition system characterized by nonlinear vegetation density–dependent regime shifts.

We characterize the dependence between lagged large‐scale climate modes and Amazon climate extremes and assess whether monthly precursor signals enable early warning of compound hot‐dry events. A copula‐based framework analyses tail dependence between major climate drivers and precipitation and temperature anomalies at multiple lags, identifying where and how far in advance extreme phases of modes of variability likely precede local extremes. Machine learning models then evaluate the predictability of compound hot‐dry events from lagged climate indices, determining which modes and lead times yield skillful forecasts. Copula modeling shows hot extremes are more likely following positive phases of El Niño‐Southern Oscillation (ENSO) and tropical Atlantic variability, with relationships more prevalent than for cold extremes. The most persistent signal occurs for March–May temperatures, when hot extremes are more likely across the Amazon following positive climate‐mode anomalies at lags up to 7 months, indicating substantial long‐lead early‐warning potential. In September–November, hot extremes are associated with prior tropical North Atlantic warming. During December–February, they occur at short lags with tropical South Atlantic warming across the basin, and at longer ENSO lags in the Planalto Amazon and Andes–Amazon foothills. In contrast, precipitation shows limited tail‐dependence with climate indices, and North Atlantic variability exhibits weak dependence with individual hot and dry extremes. Predictability of compound hot‐dry events is dominated by ENSO and peaks in December–February, especially in the western Guiana highlands, and in March–May in the lower central Amazon, with lowest skill in June–August. Reliable predictions typically require at least one climate index at 1‐month lead.

Precipitation is widely perceived to drive river flow (discharge), yet many rivers have experienced declining flow despite increasing precipitation, or vice versa. Here we quantify the prevalence of such diverging trends and identify their drivers. Using the Caravan data set and Theil–Sen regression, we analyzed long‐term trends in precipitation and river discharge at 10,179 sites worldwide (1951–2020). River discharge declined at 48% of sites, while precipitation declined at 52%. Diverging trends occurred at 27% of sites, 40% of which showed declining discharge despite increasing precipitation; their magnitude increased with aridity. A random forest classification model identified the frequency of low (or high) precipitation days and runoff ratio as the most influential drivers of strong divergence, highlighting the importance of precipitation extremes beyond the extensively discussed role of evapotranspiration. These findings reveal widespread decoupling of precipitation and river flow, highlight heightened water risks in drylands, and underscore the need for sustained river discharge monitoring, as precipitation trends alone are insufficient to predict changes in water availability.

Mesoscale convective systems (MCSs) are powerful contributors to extreme precipitation, often resulting in flash floods. However, the extent to which these systems are influenced by natural modes of climate variability is still not fully understood. In this study, we found that the occurrence of extreme precipitation from MCSs nearly doubles during the intense positive phase of the Baroclinic Annular Mode (BAM). The positive phase of BAM slows down the zonal translation speed of extratropical cyclone (ETC) trajectories, creating a favorable dynamical environment for extreme MCS precipitation. Furthermore, we discovered that when ETCs and MCSs coincide, they account for more than 90% of extreme precipitation during springtime in the U.S. These multiscale interactions between the planetary‐scale BAM, synoptic‐scale extratropical cyclones, and MCSs manifest as significant regional impacts on extreme precipitation.

East Asian Summer Monsoon (EASM) varied at orbital and millennial timescales during the Last Deglaciation (∼20–11 ka). The uniform δ 18 O response in East China is often linked to EASM circulation that drives a precipitation dipole in North and South China on orbital scale. Yet the relationship between stalagmite oxygen isotopes (δ 18 O c ) and regional rainfall remains ambiguous at millennial timescales. Combining proxy records with isotope‐enabled transient climate simulations (iTraCE), we demonstrate a decoupling between changes in δ 18 O c and regional precipitation in North China during millennial‐scale events. Although δ 18 O c responds uniformly across East China to AMOC changes and is correlated with South China rainfall, North China rainfall is instead largely driven by insolation, with negligible millennial‐scale variability. This reflects that East China δ 18 O c is driven by AMOC‐induced shifts in moisture source δ 18 O. Our findings emphasize that δ 18 O c records require forcing‐specific interpretation in East Asia across timescales.

Increasingly frequent extreme weather events are heightening the chances of mosquito‐borne diseases outbreaks, posing a notable threat to human health. However, how extreme weather influences the transmission dynamics of mosquito‐borne diseases remains poorly understood. Here, we integrate heat waves and effective precipitation as parameterization schemes into an SEI‐SEIR dynamic model to quantify their driving effects on chikungunya transmission. Based on the 2025 chikungunya fever outbreak in Foshan, the model successfully reproduced the observed transmission dynamics. Attribution analysis revealed that heat waves exerted a dual effect on transmission. Mosquito suitability declined by 9.7%, yet accelerated viral replication and altered human behavior generated a positive forcing that drove a net 76% increase in infected cases. Effective precipitation regulated outbreak scale nonlinearly, dispersed rainfall increased mosquito abundance by 205% and amplified transmission by 10.5%. Our research offered new insights into the impact of extreme weather on mosquito‐borne infectious diseases.

Runoff responds differently to rainfall events depending on antecedent wetness conditions and catchment properties. One major characteristic of the response is the runoff event timescale, herein defined as the ratio of event volume to peak runoff rate. Since the event timescale essentially reflects the event runoff shape (e.g., prolonged or flashy), analyzing this metric and its main drivers can potentially help identify fast and slow runoff generation mechanisms in a catchment. Yet, large‐sample studies assessing the spatiotemporal variability of the event timescale and its controls in different climatic regions remain scarce. Here, we quantify how hydro‐meteorological event conditions and physical catchment attributes influence runoff event timescales in catchments with contrasting climates. We conducted an event‐based analysis in 388 snow‐free catchments in the contiguous US, separately for wet and dry climate types based on aridity. We demonstrate that spatiotemporal drivers of the event timescale significantly differ between wet and dry groups. In wet catchments, the temporal variability of the timescale was primarily determined by rainfall event characteristics, particularly rainfall event duration. While rainfall characteristics were important in the dry group as well, antecedent soil moisture was more important in predicting the timescale in dry than in wet catchments. The catchment‐median timescale was influenced by topography, soil and climate attributes. Physical attributes such as catchment area were mostly important in wet catchments, whereas mean potential evapotranspiration had the strongest influence on the median timescale in dry catchments. Our results imply that the potential controls on event timescales vary with catchment climate type.

Runoff threshold behavior is widely reported in event-based hydrological studies, but its interpretation and cross-catchment variability remain unresolved because threshold metrics, values, and process interpretations vary among studies, climates and landscape settings. This study synthesizes reported storm-runoff thresholds from 138 experimental catchments worldwide, as well as reported dominant runoff mechanisms, documented wetness-dependent mechanism transitions, and soil-geology-hydrogeology associations. Across the reviewed literature, threshold-like responses were identified using rainfall metrics (e.g., event rainfall amount and rainfall intensity), hydrological-state metrics (e.g., antecedent or within-event soil moisture, storage, and groundwater level), and composite rainfall–state indicators. Hydrological-state and composite indicators were reported more frequently than rainfall-only metrics. Subsurface- and saturation-related mechanisms were most frequently reported, particularly among studies in humid catchments. Among the catchments with explicitly documented event-scale transitions in runoff generation mechanisms, shifts from surface-dominated responses toward saturation-, subsurface-, or shallow-groundwater-influenced responses were more frequently reported as catchment wetness increased within the reported transition subset, although reverse and context-dependent pathways are hydrologically possible. Co-occurrence analysis indicates that reported mechanisms are associated with soil-depth, texture, permeability, lithology, and hydrogeological descriptors, which we interpret as structural contexts that condition state-dependent functional connectivity. Together, the synthesis supports a connectivity-based framework in which rainfall forcing interacts with catchment state and structural constraints to activate or connect runoff pathways.

As part of NASA’s Global Precipitation Measurement (GPM) ground validation program, a multi-year winter field study in Storrs, Connecticut brought a unique opportunity to study the long-lasting problem of precipitation phase determination within retrieval algorithms. This study evaluates the phase algorithms of NASA’s Integrated Multi-satellitE Retrievals for GPM (IMERG) dataset, the flagship GPM global precipitation product, and NOAA’s Multi-Radar Multi-Sensor (MRMS) dataset, which is frequently used to validate IMERG precipitation rates over the continental United States. The study selected ten long-lasting phase transition events where rain and snow were recorded for more than one hour with the presence of mixed precipitation and focused on two diverse events. Using the PARSIVEL disdrometer phase as a reference, phase transition occurs at around wet-bulb temperature of 0.5˚C, which agrees with prior studies. IMERG and MRMS phase algorithms performed better when using High Resolution Rapid Refresh (HRRR) and European Center for Medium-Range Weather Forecast Reanalysis (ERA5) temperatures than Modern-Era Retrospective Analysis for Research and Applications reanalysis (MERRA2) temperatures. Overall, IMERG Version 07 performed better than its predecessor Version 06. This study also evaluated HRRR deterministic and probabilistic phase algorithms, which performed better than HRRR temperature-based MRMS and IMERG algorithms, respectively.

Compound heatwave-extreme precipitation (CHWEP) events, characterized by the rapid succession of heatwaves and extreme precipitation, pose escalating compound and cascading hazards. Yet, global-scale comparisons of their spatiotemporal evolution relative to single extremes remain limited. This study systematically examines the changes in CHWEP and corresponding single extremes from 1980 to 2100 using climate observations and projections under SSP (Shared Socioeconomic Pathway) 2-4.5 and SSP5-8.5 scenarios. We find that CHWEP exhibit higher frequency, stronger precipitation, and longer heatwave duration in mid-to-high latitudes of the Northern Hemisphere, while tropical CHWEP feature more intense heatwaves than single heatwave events. These spatial contrasts persist in future projections. Under both scenarios, CHWEP and single extreme metrics intensify globally by 2056–2100, with post-heatwave precipitation exceeding that of single precipitation extremes, particularly under SSP5-8.5, highlighting sensitivity to greenhouse forcing. Critically, the co-occurrence is non-random, indicating an emerging physical linkage. In the tropics, the likelihood of extreme precipitation following heatwaves increases markedly. Our findings demonstrate that CHWEPs are evolving into a distinct, intensifying hazard class, necessitating their integration into climate resilience, early warning, and adaptation frameworks.

Hot droughts are intensifying under anthropogenic warming, yet whether extreme heat systematically alters the triggering of abrupt drought‐to‐pluvial transitions and their downstream impacts remains poorly understood. Here, we identify global drought‐to‐pluvial abrupt transition (DPAT) events and isolate the influence of antecedent thermal conditions by comparing hot‐drought‐triggered DPATs (DH‐DPATs) with transitions preceded by droughts without concurrent heat. We find that hot drought related transitions display broader hotspots and substantially higher occurrence, with frequency increasing from 66.7% to 316.7% across the interquartile range and a higher conditional transition probability (global mean: 44.89% vs. 38.95%). They also transition faster, sustain longer post drought heavy rainfall, and produce stronger rainfall intensity. Mechanistically, although both event types are associated with enhanced moisture convergence, antecedent heat imposes an additional thermodynamic preconditioning characterized by deeper boundary layers and higher convective instability, making comparable moisture recovery more likely to trigger deep convection. These heat‐mediated transitions amplify ecosystem carbon losses, intensifying reductions in gross primary productivity and solar‐induced chlorophyll fluorescence by about 43.7% and 26.8%, respectively, with the strongest relative impacts in croplands. They also contribute to increasing population and managed‐land exposure. Under future warming, DPATs are projected to shift further toward hot‐drought‐dominated pathways, with increases in both the contribution and transition likelihood of DH‐DPATs. These results highlight a thermodynamic pathway through which hot droughts increase the likelihood and impacts of abrupt drought‐to‐pluvial transitions worldwide.

Predicting changes in urban pluvial flood hazards under climate warming is crucial for risk mitigation and disaster management. A key challenge in simulating future urban flood hazards is the scarcity of high-resolution rainfall projections, particularly at the sub-daily and kilometer scales required for hydrodynamic modeling. We present a cascading process-informed framework that requires minimal observed climatic data, enabling scenario analysis even in data-scarce cities. This framework consists of a distribution‐based spatial quantile mapping (DSQM) method to morph observed rainfall fields conditioned on temperature changes, a stochastic storm transposition (SST) method to account for the spatial variability of urban rainfall, and a rain‐on‐grid hydrodynamic model (AUTOSHED) for efficient simulation of urban pluvial floods at high spatio-temporal resolution. The framework allows the generation of stochastic rainfall fields under different rainfall return levels and regional warming levels. It supports the quantification of changes in future urban flood statistics with detailed hazard maps of inundation depth, duration, and flow velocity. We select the metropolitan area of Beijing (300 km 2 ) as a case study area and utilize gridded hourly and 1 km rainfall data to simulate flood evolution at 5 min and 5 m resolution under regional warming levels of 1, 3, and 5 °C relative to the period 1998–2019. Our results show that with rising temperatures, regional storms tend to become more intense but smaller in spatial extent, which may in turn drive increased local flood depth, accelerated flow velocity, and deeper inundation, collectively elevating pluvial flood risk. Specifically, mean rainfall intensity increases by 6 %, 11 %, and 20 % (respectively with the warming levels), peak flood depth exhibits a nonlinear increase of 4 %, 7 %, and 8 %, due to the complex interactions of reduced storm area, increased storm intensities, and rainfall spatial variability. The proposed DSQM-SST-AUTOSHED framework offers a data-driven, physically grounded, and efficient approach to assess urban flood risk under regional warming. It only requires observed rainfall fields and temperature datasets, which are readily accessible from public sources, making the approach easily extendable to other cities.

We introduce OpenMesh, a publicly available dataset of wireless signal measurements from the NYC Mesh, a community-run communication network in New York City (NYC). While originally designed for affordable internet access, these links can be used opportunistically for high-resolution weather monitoring in diverse settings, including dense urban areas, providing 1 min sampling of signal strength measurements with dense spatial coverage across the study region. Spanning eight months of measurements (November 2023 to June 2024), the dataset comprises 103 wireless links in Lower Manhattan and Brooklyn, operating in three primary frequency ranges: 5–6 GHz (C-band), 24 GHz (K-band), and 58–70 GHz (V-band), part of the millimeter-wave (mmWave) spectrum. Our analysis incorporates meteorological records from two primary sources: (1) 37 Weather Underground (WUnderground) personal weather stations (PWS) across Manhattan and Brooklyn with 5 and 15 min sampling, and (2) three Automated Surface Observing System (ASOS) stations. The study period saw total rainfall of approximately 900 mm and included diverse weather events, from intense rain to snowstorms in winter 2023–2024. These events were captured by the NYC Mesh wireless network, with intense rainfall causing attenuation up to 30 dB and frequent outages, especially on high-frequency V-band links. This highlights the trade-off of using high-frequency bands in wireless networks: while precipitation might cause severe channel disruptions, this greater sensitivity simultaneously makes them effective opportunistic weather sensors. The OpenMesh dataset is available at https://doi.org/10.5281/zenodo.15287692 (Jacoby et al., 2025b). By publishing the dataset and demonstrating its usability, we aim to encourage further research that leverages wireless networks for real-time urban weather sensing.

In the development of satellite precipitation climate data records, enormous effort has been expended to ensure that satellite precipitation products (SPPs) match the most accurate in situ gauge measurements. In this work, we evaluate the accuracy of satellite precipitation estimates covering a range of rainfall accumulation from the subdaily to the monthly scales. Using the U.S. Climate Reference Network (USCRN) as a reference, results show that Climate Prediction Center morphing technique (CMORPH) and Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) satellite products performed better at accumulation scales greater than 12 h than at smaller time scales. Both products exhibited strong contrast between the western and eastern halves of the contiguous United States (CONUS), with the best results obtained over the eastern United States. Normalized SPP estimations of rainfall event maxima for heavy precipitation events—those at or above the 90th percentile at each station—are consistent across the CONUS, with SPP estimates generally below the station observations. Residual analysis at the 90th percentile indicates that SPPs tend to underestimate extreme precipitation when compared to in situ observations, with very few exceptions. Generally, the SPPs underestimate the top-10 percentile observations by a factor of around 0.25–0.5 with respect to the in situ 90th percentile threshold. For maximum precipitation, SPP estimates diverge from gauges significantly at the 1–3-h accumulation time scales. Between 6 and 12 h, the SPP estimates tend to stabilize, and the differences between the SPPs and USCRN are reduced thereafter regardless of the location or for the entire CONUS. The use of satellite precipitation estimates for extreme event quantification at time scales shorter than 12 h should be done with caution.

Satellite-based precipitation products (SPPs) face persistent challenges over mountains due to complex orographic processes. This study uses high-resolution radar-gauge precipitation data and environmental reanalysis parameters to evaluate IMERG V07B Final Run (IM-F) performance across six mountain ranges (U.S. West Coast, Rockies, East Coast; French Alps, Central Massif, Pyrenees). We condition the analysis on environmental clusters defined by orographic upward motion, moisture flux convergence, and stability, to address two questions: (1) To what extent do environmental parameters explain the distribution and extremes of radar-observed precipitation in each mountain range? (2) How much of the variation in IM-F retrieval error across these mountain ranges is explained by environmental parameters? Results show that environmental conditions strongly control precipitation distributions – for instance, convective windward regimes yield the highest median and 99th-percentile rain rates, whereas stable, stratiform conditions produce much lower rainfall totals. Conditional validation reveals that a substantial portion of IM-F error is attributable to these environmental factors: precipitation detection skill and bias vary systematically with cluster conditions. Cluster 1 (unstable upslope flow) produces frequent heavy precipitation that IM-F underestimates when cloud ice is minimal, whereas Cluster 4 (stable, downslope conditions) is associated with light stratiform drizzle that IM-F often misses. These findings expose how orographic lift, moisture convergence, and stability interact to shape both precipitation extremes and satellite retrieval biases. This is the first study to systematically dissect IM-F error by environmental regimes across multiple global mountain ranges, providing novel insight into physical drivers of SPP errors. Building on these insights, we propose adaptive algorithm enhancements – for example, incorporating orographic uplift and moisture convergence indicators – to improve mountainous precipitation estimation. Our results underscore a new process-informed pathway for refining satellite QPE in complex terrain.

Precipitation is important for hydrological monitoring and modeling. The accuracy of mean areal precipitation (MAP) estimation relies largely on the configuration of the precipitation network. This study proposes a spatial pattern–based framework for optimizing rain gauge networks that uses the climatological precipitation field from high-quality reanalysis data as an external benchmark to evaluate MAP estimation. In contrast to entropy- and variance-based methods, the framework requires no long in situ records, explicitly matches both the magnitude and the full spatial distribution of the precipitation field, and can assess as-yet-uninstrumented locations. Using the Qingyi River basin as a case study, we employ the China Meteorological Administration Multisource Precipitation Analysis System (CMPAS) fusion data to characterize precipitation spatial patterns. The framework introduces two metrics, the mean areal precipitation bias (MB) and the Kullback–Leibler (KL) divergence, to quantify differences between gauge-derived and CMPAS-derived precipitation fields. Evaluation of the current network reveals significant MAP estimation discrepancies in subbasins with high precipitation variability. Through a significance index analysis of candidate gauges and a hierarchical optimization, strategic gauge placement reduced MAP bias from up to 16% to below 1% in the optimized subbasins. An independent hydrological validation with the Xin’anjiang model showed that the optimized network improved streamflow simulation, with the basin-average Nash–Sutcliffe efficiency increasing from 0.63 to 0.76, confirming that better MAP estimation propagates to improved hydrological prediction. The framework is computationally efficient and applicable to both network expansion and rationalization.

Tropical regions may experience periodic extreme precipitation and suffer from associated periodic deluges in a warmer climate. Recent studies conducted small-domain (around 100 kilometers by 100 kilometers) atmospheric model simulations and found that precipitation transitions from a steady state to a periodic oscillation state in a hothouse climate when the sea surface temperature reaches 320 to 325 kelvin. Here, we conduct global-scale atmospheric model simulations with varying complexity. We find that tropical precipitation in convective regions already transitions to a O (10-day) periodic oscillation state with a O ( 100 mm day − 1 ) amplitude at 305 to 310 kelvin, about 15 kelvin lower than previously reported and within reach in a century under a high–carbon emission scenario. Using a low-order analytical model, we attribute the onset of the periodic extreme precipitation to the intensification of convectively coupled waves that were not considered in the previous small-domain simulations. Our mechanism highlights the importance of the interaction between convection and atmospheric wave dynamics for climate change.

The Gravity Recovery and Climate Experiment (GRACE) and its Follow-On (GRACE-FO) missions provide observations of terrestrial water storage (TWS) dynamics from regional to global scales. However, their coarse spatio-temporal resolution limits the applicability in many hydrological studies. A joint collaboration between the National Aeronautics and Space Administration (NASA) and the European Space Agency (ESA), known as the Mass Change And Geosciences International Constellation (MAGIC), aims to launch a new paired mission, i.e., GRACE-C and NGGM (Next Generation Gravity Mission), to improve the monitoring of extreme events such as floods and droughts. The primary objective of this study is to examine the impact of the expected improvement in the spatial-temporal resolution and accuracy of NGGM and MAGIC on precipitation estimation by developing multiple synthetic experiments over Europe. The study employed the well-known SM2RAIN algorithm to estimate the precipitation accumulated between two consecutive TWS measurements. The total amount of water in the soil from the fifth generation ECMWF reanalysis for the land (ERA5L) is used as a proxy of TWS over the period 2003–2012, while ERA5L precipitation serves as a reference dataset. The results showed that SM2RAIN exhibited satisfactory performance at a daily temporal resolution, with a mean correlation coefficient of 0.86. Good agreement was obtained across most of Europe except in some areas of northern Italy, northeastern Europe (Estonia, Latvia) and some coastal regions. Synthetic experiments were developed by degrading the temporal resolution (5–10, 15, and 30 d) and by adding Gaussian errors to TWS data ranging from 1 to 40 mm. The results showed robust performance under moderate temporal degradation (5–10 d) and low measurement errors (< 5 mm), with correlations generally exceeding 0.75. However, performance deteriorates significantly when errors exceed 10–20 mm, with correlations dropping below 0.40 for an error of 42 mm. These findings identify critical design thresholds for NGGM/MAGIC missions, demonstrating that achieving a measurement accuracy better than 5–10 mm is crucial for reliable precipitation estimation across diverse European hydroclimatic conditions.

Climate change is causing the magnitudes of extreme sub-daily precipitation events to increase. The ability to predict changes to these precipitation extremes is crucial for disaster preparedness. The TENAX model was proposed to predict return levels of sub-daily extreme precipitation under climate change based on the projected temperature shifts. It combines a Weibull distribution with an exponential temperature dependence in the scale parameter, accounting for the Clausius–Clapeyron relation, with an explicit representation of the temperatures during precipitation events. The Weibull distribution's shape parameter could also have a temperature dependence, which would mean that the tail heaviness changes with temperature. This implies that the rarest events may increase at faster rates. However, implementing this dependence increases the number of parameters to be estimated, affecting the model's accuracy. Here, we use hourly data from thousands of rain gauges in Germany, Japan, the UK, and the USA to assess the dependence of the Weibull shape parameter on temperature, exploring how it should be implemented in the TENAX model. We find that there is a significant dependence in many stations and that the magnitude and sign of the dependence have regional patterns. In the majority of stations, the sign is negative, implying that rarer events intensify with temperature at a higher rate. However, Monte Carlo simulations show that including this dependence without careful consideration may lead to overestimation of precipitation return levels and increase the model uncertainty. The dependence should therefore be introduced with caution, in the context of surrounding stations.

Karst aquifers are notoriously heterogeneous, making it challenging to characterize recharge dynamics using traditional, static indicators. Percolating (drip) water records, when analyzed at high temporal resolution, offer insights into flow mechanisms in karst vadose zones. This study utilized karst caves to observe drip rates and identify recharge behaviors. An integrated time series analysis was used combining fast Fourier transform (FFT), cross wavelet transform (XWT), change point analysis (CPA), and clustering. This study analyzes one year of daily drip water and rainfall data from 46 monitoring sites across six karst regions in southeastern Australia. Five major types of recharge emerged from this analysis, including mixed flow dominated by soil (8.7%) or epikarst (37.0%), fracture‐dominated flow (17.4%), storage‐controlled flow (13.0%), and threshold‐controlled flow (23.9%). Across regions, recharge behavior varies with climate, lithology and karst maturity, reflecting contrasting storage and flow conditions. These categories capture temporal patterns, rainfall‐drip synchrony, and abrupt shifts in recharge behavior across diverse karst environments. Notably, the FFT method highlighted overall periodicity of drip counts, while XWT and CPA revealed non‐linear responses and flow paths transitions, often linked to rainfall thresholds and seasonal shifts. These results demonstrate the complexity and heterogeneity of karst systems, where similar rainfall amounts can produce significantly different recharge behaviors depending on site‐specific flow paths and storage dynamics. This framework enables interpretation of drip data sets and provides a tool for karst hydrological research, with direct implications for water resource management under shifting climate regimes.

Characterizing storms that generate heavy precipitation across different timescales is essential for parameterizing and assessing stochastic weather generators, evaluating climate models, enhancing risk management, and understanding climate change impacts. This study develops a detailed climatology of precipitation and temperature time series for storms producing heavy rainfall at sub‐hourly to daily scales across the Greater Alpine Region. Using a high‐resolution data set from ∼400 climate stations with 15–44 years of records for which a storm typology is available, we identify independent storm events and describe them through multiple characteristics, including peak intensity, lifetime, depth, temporal structure, internal variability, and thermal and geographical attributes. At each climate station, we focus on storms producing the largest 10% of the precipitation intensities at durations from 10 min to 24 hr. Results show that storm characteristics vary systematically with both duration and geography. At sub‐hourly scales, the highest intensities are typically generated by short‐lived storms with front‐loaded temporal profiles, high internal variability, and warmer conditions, especially in lowland regions, and exhibit evident reverse elevation‐intensity relationship. At daily scales, the highest intensities are associated with storms of greater depth, more symmetric temporal profiles, and smaller pre‐to‐within‐storm temperature differences, particularly in the inner Alps. At multi‐hour durations, distinct spatial patterns highlight the varying dominance of convective versus stratiform precipitation mechanisms. This climatology provides new insights into storm behavior in complex terrain and can inform precipitation modeling, climate impact assessments, and hydro‐geomorphological studies.

The Amazon's arc of deforestation is a hotspot of rapid land‐cover change. Using 20 years of MODIS imagery combined with cloud property retrievals, CERES radiative fluxes, and ERA5 data, we show that deforestation consistently enhances low‐level cloudiness over non‐forest relative to nearby forest during the Amazon dry season. Non‐forest‐preference cloud scenes overwhelm those of forest‐ or no‐preference, with MODIS imagery often revealing clouds strikingly echoing the underlying land cover pattern. While the sign of this cloud response is robust, its magnitude is state‐dependent, varies with the background atmospheric state. Within the non‐forest‐preference cloud scenes, two distinct cloud regimes emerge: a warmer and drier regime, where higher sensible heat and lower latent heat produce sparse, shallow clouds with low cloud fraction (CF), liquid water path (LWP), and optical thickness (COT); and a cooler and moister “deepening” regime, where enhanced latent heat supports clustered, deeper clouds with higher CF, LWP, COT, greater precipitation, and stronger net cooling at the top‐of‐atmosphere (TOA). Our findings reveal that while deforestation triggers an enhancement in cloudiness, the nature of this response is tightly coupled to the background climate conditions, with implications for both the radiative energy budget and the hydrological cycle. As the Amazon trends toward a warmer and drier climate, a shift in cloud regime prevalence could diminish the TOA cooling and precipitation currently provided by the deepening cloud regime.

South China is one of the most frequently affected regions by landfalling tropical cyclones (TCs) worldwide. Owing to its complex topography and environmental conditions, TC precipitation in this region typically exhibits pronounced asymmetry, with substantially stronger rainfall on the southern‐side of the TC center. Here, we show that precipitation asymmetry has decreased by 29% during 1998–2024. Specifically, a significant increase in the area‐averaged precipitation rate is observed on the northern‐side of TC center, accompanied by a decrease on the southern‐side. Diagnostic analyses indicate that this change is closely associated with the weakening of the vertical wind shear (VWS) at an average rate of 0.046 m s −1 per year, along with concurrent changes in upper‐ and lower‐level divergence and vertical motion. These dynamical changes collectively contribute to enhanced precipitation on the northern‐side of TC center, suggesting an increasing potential risk of rainfall impacts in this region.

In mountainous regions orographic effects create strong horizontal gradients of various rainfall statistics such as the frequency of occurrence, the distribution of intensity and the structure of spatial correlation. However, most statistical models of daily rainfall assume spatial stationarity (i.e., the spatial homogeneity of rainfall statistics) and are therefore not well suited for studying the highly non-homogeneous characteristics of orographic rainfall. To overcome this limitation, we design a non-stationary trans-Gaussian geostatistical model for the analysis of daily rainfall fields over complex topography. This framework infers rainfall statistics from sparse rain gauge observations, simulates realistic rainfall fields after calibration and stochastically interpolates rain gauge observations to create rainfall maps. The performance of the model is assessed with data from the Island of Hawai`i where extreme spatial gradients in rainfall are observed. Results demonstrate that the non-stationary trans-Gaussian model can skillfully reproduce orographic rainfall statistics as well as their variations in space.

The link between rainfall extremes, usually defined as a given percentile or for a given return period, and temperature has been widely investigated using measurement data and/or climate model outputs and notably convection permitting model outputs. A focus was notably on whether findings are consistent with Clausius-Clapeyron relation. Here we investigate more generally how rainfall variability across scales change with temperature, relying on the scale invariant framework of the Universal Multifractals. Extremes, which can be derived from multifractal features are embedded in the study. Rainfall and temperature data from three high resolution measurement campaigns that took place in Northern France between 2016 and 2025 are used. Scaling behaviour is confirmed on two distinct ranges of scales, first at event scale (30 s – 1 h) and then up to synoptic scale (roughly 11 d). Then we find that across both ranges of scales, the scale invariant maximum observable singularity is positively associated with sample-mean surface temperature, with weak individual-sample correlations and with seasonality that cannot be fully disentangled from temperature. This provides a scale-invariant observational complement to the existing multifractal-extremes literature on climate-forced rainfall changes. It provides a framework to interpret previously commonly reported trends of a scale dependence of the rate of increase of extremes with temperature.

Mid-late Holocene hydroclimate changes in the Thar Desert in northwest India have been linked with the Indus cultural transformation. Regional hydroclimate reconstructions present a spatially heterogeneous picture; in particular, the role of seasonality remains unclear. Here, we use multi-model simulations from the Paleoclimate Modelling Intercomparison Project Phase-4 (PMIP4) to evaluate the seasonality of mid-Holocene precipitation and mechanisms governing spatial moisture variability. Results show strong agreement with proxy data on strengthened summer monsoon across the Thar Desert during the mid-Holocene, with north receiving double the effective precipitation than south, which is synchronous with development of agriculture alongside continued foraging. High winter rainfall changes in the south as compared to north explains the spatial heterogeneity in paleoclimate records and possibly the distinct cropping strategies that emerged across the Indus occupied regions. These findings highlight the critical role of seasonal precipitation contrasts in shaping both environmental conditions and cultural adaptations in climatically sensitive regions.