PIGMLD: A Physics-Aware Geospatial Machine Learning for High-Resolution Extreme Precipitation Reconstruction

Reliable high-resolution precipitation is crucial for monitoring hydrologic extremes and guiding climate-risk decisions, especially for compound events such as dry-to-wet “whiplash” . However, satellite precipitation products are often too coarse (5–25 km) and show strong region- and intensity-dependent biases, limiting their value for local hazard assessment. We develop a physics-aware geospatial machine-learning downscaling and fusion framework (PIGMLD) to generate 1-km daily precipitation over China for 2000–2020 by combining 10-km GPM IMERG with sparse gauges, ERA5-Land precipitation, and physically interpretable covariates linked to moisture, clouds, and land–atmosphere coupling. Validation against independent gauges across China and nine major basins shows broad skill gains (84.1% of stations with KGE > 0.60), improved event detection, and reduced bias; improvements are smaller in terrain-complex, gauge-scarce regions but remain useful. Performance gains are strongest for extremes, with large RMSE reductions for heavy and torrential rainfall and substantial bias corrections for both dry and wet percentile-defined extremes. Overall, PIGMLD provides more reliable 1-km precipitation to better characterize hydroclimate extremes and support water hazard related risk assessment.