Spatial Pattern Regression (SPR) is a new way to reconstruct daily weather fields in regions with few measurement stations. Our approach combines information from past high-resolution simulations with available observations to produce more accurate maps of precipitations and temperature. Tests on both synthetic and real data show clear improvements over common methods, especially when stations are sparse, helping support better hydrological and climate studies.

Groundwater drought impacts depend on rainfall deficits, aquifer storage, and human activities. We analyzed rainfall and groundwater data from three aquifers to compare drought timing, duration, recovery, and spatial patterns. The deepest aquifer had the longest deficits and showed no signs of recovery during the study period. Our framework can support groundwater monitoring and management under combined climate and human pressures.

This study evaluates the ability of the convection-permitting regional climate model AROME (Applications of Research to Operations at Mesoscale) to reproduce precipitation extremes and their trends, at daily and hourly scales in France, over 1959–2022. Overall, the results highlight the value of the explicit-convection model for extreme precipitation studies, while also highlighting its limitations for convective extremes.

We studied how well simple bucket-type models can reproduce observed river flow using large data sets from many regions in the world. Model performance varies widely depending on local conditions, so fixed performance thresholds are misleading. To better judge model performances, we propose lower and upper benchmarks. These benchmarks help to better understand what level of model performance is achievable and, thus, enable us to compare models across different catchments.