Enhanced Hourly Precipitation Estimation Using a Geographically Constrained Multi‐Source Fusion Network With Cross Attention

Abstract Precipitation plays a crucial role in the global hydrological cycle, and its irregular distribution contributes directly to natural hazards such as floods, waterlogging, and droughts. Satellite remote sensing has emerged as an effective tool for global precipitation monitoring. However, accurately estimating hourly precipitation from satellite observations remains a major challenge due to its high spatiotemporal variability. To address this challenge, we propose a novel framework—Geographically constrained multi‐source Fusion Network with cross Attention (GeoFNA)—designed to enhance the accuracy of hourly satellite precipitation estimates. GeoFNA integrates a spatiotemporal convolutional network with cross‐attention mechanisms to effectively capture complex spatiotemporal patterns and nonlinear relationships across multi‐source precipitation data sets and auxiliary variables. To further improve model robustness, geographically associated input constraints and weight constraints are incorporated to account for the skewed distribution and rapid variability of hourly precipitation. Results demonstrated that GeoFNA outperformed three baseline models, achieving significantly higher agreement with in situ measurements. Specifically, GeoFNA increased the Pearson Correlation Coefficient from 0.38 to 0.89 and reduced the Mean Squared Error from 2.39 to 0.50 (mm/h) 2 compared to the original satellite precipitation data. Additionally, GeoFNA exhibited strong spatial robustness, underscoring its potential for accurate and reliable quantitative precipitation estimation. These advancements pave the way for improved hydrological modeling and meteorological research.