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Vicente‐Serrano et al. (2025) Characteristics of widespread extreme precipitation events in Peninsular Spain and the Balearic Islands: spatio-temporal dynamics and driving mechanisms
This study investigates the spatiotemporal trends and atmospheric drivers of widespread extreme precipitation events (WEPEs) in Spain from 1961 to 2022. It finds no significant trend in WEPE frequency or intensity, but identifies five distinct synoptic patterns and associated moisture sources that govern their regional distribution.
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Bouizrou et al. (2025) The potential of novel remote sensing evapotranspiration data and global soil maps for SWAT+ agro-hydrological modeling in data-scarce regions of the North Mediterranean
This study utilized the SWAT+ model with novel remote sensing evapotranspiration (RS-ET) data, a high-resolution global soil map (DSOLMap), and detailed agricultural practices for agro-hydrological modeling and multisite calibration in four ungauged watersheds in the data-scarce Messinia region, Greece. The findings demonstrate that integrating DSOLMap and GLEAM RS-ET significantly improved model performance (Nash-Sutcliffe Efficiency > 0.5; Percent Bias < ±15 %) compared to local soil maps and MODIS RS-ET, providing a valuable tool for water resource management.
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Lin et al. (2025) The Positive Pacific–Japan Pattern Drives Compound Heat and Dry Extremes in Summer over Taiwan
This study examines how the positive phase of the Pacific–Japan (PJ) pattern triggers compound heat wave–drought events in Taiwan via land–atmosphere feedback mechanisms.
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Li et al. (2025) Divergent Seasonal Biophysical Effects Induced by the Three Gorges Reservoir
## Identification - **Journal:** Water Resources Research - **Year:** 2025...
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Berényi et al. (2025) Validation of Euro‐ CORDEX Simulations Focusing on Mean and Extreme Precipitation in European Plain Areas
This study assesses the performance of ten historical Euro-CORDEX simulations against observational data for extreme precipitation indices across 14 European plain regions from 1970 to 2005. It finds a general overestimation of precipitation with seasonal and regional biases, but concludes that the models are capable of reproducing historical patterns and the ensemble is adequate for future climate analysis.
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Mouassom et al. (2025) Convolutional Neural Network‐Based Insights Into Extreme Precipitation Regional Dynamics Over Central Africa Using Moisture Flux Patterns
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Han et al. (2025) Recombining past event precipitation and antecedent catchment states generates unprecedented floods
This study introduces a "perfect storm" approach to generate plausible, unprecedented flood scenarios by recombining historical extreme precipitation events with antecedent catchment soil moisture conditions in Germany, demonstrating that these scenarios can significantly exceed historical flood magnitudes and damages.
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Saeedi et al. (2025) Introducing a new clustering-based method for regionalization framework for continental-scale rainfall estimates from soil moisture dynamics using machine learning methods
This study introduces a novel calibration-free regionalization framework for continental-scale rainfall estimation from soil moisture dynamics, combining unsupervised (K-means) and supervised (rainfall-intensity classification) clustering with a genetic algorithm. The framework, demonstrated with the SM2RAIN-Net Water Flux (NWF) algorithm over the contiguous United States (CONUS), significantly outperforms classical SM2RAIN methods by achieving a 20 % improvement in Nash–Sutcliffe efficiency and a 10 % reduction in root mean square error.
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Liu et al. (2025) Global Evapotranspiration Retrieval Using Fengyun‐3D Passive Microwave Measurements With Genetic Algorithm Optimization
This study developed a new global evapotranspiration product ($ET_{FY3D}$) by using a genetic algorithm to optimize a passive microwave retrieval method based on FY-3D satellite observations.
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Lee et al. (2025) Multiple Cloud Feedbacks in a Global Model From a Single Perturbation Experiment
This study demonstrates that different implementation choices for calculating cloud feedbacks within a single GEOS model perturbation experiment lead to a wide range of resulting feedback values.
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Zhang et al. (2025) Cascading Spatial Scales in the Hydrological Cycle Over Africa
This study quantifies the spatial scales of precipitation, soil moisture, and vegetation across Africa from 2016 to 2023 to investigate the existence of a spatial cascading link in the hydrological cycle. The results demonstrate that spatial scales increase sequentially from precipitation to soil moisture to vegetation, with the strongest coupling observed between soil moisture and vegetation.
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Diaz et al. (2025) Evaluation of daily stream temperature predictions (1979–2021) across the contiguous United States using a spatiotemporal aware machine learning algorithm
This study developed and evaluated a recurrent graph convolution network to predict daily minimum, mean, and maximum stream temperatures across over 50,000 stream reaches in the contiguous United States (CONUS) for 42 years (1979–2021). The model achieved satisfactory performance with reach-level root mean square errors (RMSE) below 2 °C and robust uncertainty quantification, providing the most spatially complete stream temperature modeling to date for water availability assessments.
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Chen et al. (2025) Diurnal Cycle of Extreme Rainfall Over the Dabie Mountain in Summer Under Typical Synoptic Patterns and Associated Mechanisms
This study investigates the influence of synoptic patterns and topography on extreme summer rainfall in the Dabie Mountains from 2008 to 2020, identifying two distinct patterns (P1 and P2) that modulate rainfall duration and intensity.
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Liu et al. (2025) Time of Emergence of Record‐Shattering Compound Heatwave‐Extreme Precipitation Events and Their Socio‐Economic Exposures
This study quantifies the global time of emergence (ToE) for record-shattering compound heatwave-extreme precipitation events (CHEPs) and assesses the associated socio-economic risks. It finds that while the majority of global areas will experience these events by 2100, the poorest regions face the most significant population and GDP exposures.
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O'Sullivan et al. (2025) Efficient Likelihood and Machine‐Learning Models for Spatiotemporal Rainfall Estimation and Imputation
The study develops and evaluates a likelihood-based imputation method and a DeepKriging approach to efficiently handle missing values in large spatiotemporal precipitation datasets.
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Huang et al. (2025) Thermodynamical and Dynamical Background Characteristics and Microphysical Structures of the Mesoscale Convective Systems Over the Tibetan Plateau: A 7‐Year Statistic
This study analyzes the vertical structure of Tibetan Plateau mesoscale convective systems (TP_MCSs) using a 7-year database, ERA5 reanalysis, and satellite data to identify the thermodynamic, dynamic, and microphysical drivers of their intensity and precipitation.
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Pan et al. (2025) Intraseasonal Variations in Spring East Asian Subtropical Jet: Role of Mid–High Latitude Wave Trains and Influence on Rainfall Anomalies in China
This study identifies eight phases of the spring East Asian subtropical westerly jet (EAWJ) intraseasonal variability and demonstrates that its northward shift significantly increases precipitation and extreme weather events in the Yangtze–Huai River valley.
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Qin et al. (2025) Warm Rain Processes Affected by Explosion: Insights From First 3D Large‐Eddy Simulation and Implications for Weather Modification
This study utilizes a coupled explosion and 3D large-eddy simulation model to demonstrate that explosions in warm cumulus clouds accelerate rain formation while simultaneously reducing maximum rain intensity.
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Yan et al. (2025) Simulating Continental Dust on a Hard Snowball Earth: 1. Limited Dust Emission
This study simulates continental dust emissions during the "snowball Earth" period, finding that emissions were significantly lower than previously estimated due to the suppressive effects of ice and frozen soil.
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Ghosh et al. (2025) Change in convection and thunderstorm occurrences over the Indian subcontinent during the COVID-19 pandemic
This study utilizes the 2020 COVID-19 lockdowns as a natural experiment to demonstrate that reduced anthropogenic aerosols in the Indian subcontinent increased thunderstorm frequency via radiative destabilization, while simultaneously decreasing the electrification (lightning flashes) of individual storms.
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Vogel et al. (2025) Do Observations Support Ideas Behind Common Mass Flux Closures?
This study evaluates two common cloud-base mass flux closure models using EUREC4A campaign data, concluding that Turbulence Kinetic Energy (TKE) is the primary driver of mass flux variability.
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Drift et al. (2025) Dependence of Convective Precipitation Extremes on Near-Surface Relative Humidity
This study uses convection-resolving simulations to demonstrate that reducing near-surface relative humidity (RH) weakens convective precipitation extremes through thermodynamic, dynamic, and precipitation efficiency mechanisms.
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Niu et al. (2025) Machine‐Learning (ML)‐Physics Fusion Model Accelerates the Paradigm Shift in Typhoon Forecasting With a CNOP‐Based Assimilation Framework
The study develops a hybrid forecasting system integrating the FuXi machine-learning model with the physics-based Shanghai Typhoon Model (SHTM) to improve short-term predictions of typhoon track, intensity, and precipitation.
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Chen et al. (2025) Alternative Interpretation of MJO Teleconnection via Dynamical Mode Decomposition
This study employs Dynamical Mode Decomposition (DMD) to analyze Madden–Julian oscillation (MJO) teleconnections, interpreting them as a spatiotemporal resonance between MJO forcing and the atmosphere's internal unforced dynamics.
