High-spatiotemporal-resolution rainfall data are crucial for investigating local terrestrial water cycles. Although remote-sensing satellite precipitation products effectively reproduces spatial patterns of rainfall, it suffers from low spatial resolution. To overcome such limitations, a two-step downscaling approach is proposed here, primarily involving correction followed by downscaling. First, 80% of the meteorological-station data is utilized to calibrate the original Global Precipitation Measurement (GPM) data, enhancing the correlation between GPM and station data. Subsequently, utilizing elevation, slope, aspect, NDVI, wind direction, water vapor, and land surface temperatur, as well as slope and aspect correction factors, as independent variables, multiscale geographically weighted regression (MGWR) and temporal lag MGWR (TL-MGWR) models were constructed. We selected the model with higher accuracy on a monthly basis, and thereby obtaining higher-precision rainfall data. Through the aforementioned steps, downscaled monthly and daily precipitation data for the study area in 2022 at a spatial resolution of 0.01° were obtained.
Our findings indicate that selectively employing suitable MGWR or TL-MGWR models on a monthly basis can effectively downscale monthly GPM rainfall data in the study area. A consideration of the lag effects of NDVI between April‒June and October‒December improved the downscaling performance of the MGWR model. This downscaling process preserved the spatial distribution of the original GPM while enhancing the spatial resolution and had lower MAE , RMSE values, as well as exhibited smaller biases. The downscaled (original) monthly precipitation data exhibited a correlation of 0.94 (0.768), with an MAE of 16.233 mm/month, RMSE of 27.106 mm/month, and bias of −0.043. Similar enhancement was likewise noted in daily precipitation, displaying a correlation coefficient of 0.863 (0.318) for downscaled (original) data, and a RMSE of 3.209 mm/day, MAE of 1.082 mm/day, and bias of −0.06. In summary, the data after downscaling, both for monthly and daily datasets, was markedly improved in accuracy. The proposed downscaling method is applicable for reconstructing high-resolution grid data at monthly and daily temporal scales in the complex terrain of the southwest China highland canyon area.

 

GPM; Downscaling; MGWR; Temporal lag; Calibration; Meteorological stations; Southwest China