A New HASM-Based Downscaling Method for High-Resolution Precipitation Estimates

Obtaining high-quality precipitation datasets with a fine spatial resolution is of great importance for variety hydrological, meteorological and environmental applications. Satellite-based remote sensing can measure in large areas but suffers from inherent bias relatively coarse resolutions. Based on the high accuracy surface modeling method (HASM), this study proposed new downscaling method, modeling-based (HASMD), to derive monthly estimates at 0.01° by Integrated Multi-satellitE Retrievals Global Precipitation Measurement (IMERG) China. A scale transformation equation was introduced HASMD, initial value set including explanatory variables related precipitation. The performance HASMD evaluated comparing results yielded HASM combined HASM, Kriging, IDW geographical weighted regression (GWR) (GWR-HASM, GWR-Kriging, GWR-IDW). Analysis indicated that performed better than other four methods. High agreement achieved values ranging 0.07 0.29, root mean square error (RMSE) 9.53 mm 47.03 mm, R2 0.75 0.96. Compared original IMERG products, improved up 47%, 14% according bias, RMSE R2, respectively. able capture variation vast region, it might be potentially applicable enhancing remotely sensed data facilitating their application scales.