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. Precipitation data with high resolution and high accuracy aresignificantly important in numerous hydrological applications. To enhancethe spatial resolution and accuracy of satellite-based precipitationproducts, an easy-to-use downscaling-calibration method based on a spatialrandom forest (SRF-DC) is proposed in this study, where the spatialautocorrelation of precipitation measurements between neighboring locationsis considered. SRF-DC consists of two main stages. First, thesatellite-based precipitation is downscaled by the SRF with the incorporation ofhigh-resolution variables including latitude, longitude, normalizeddifference vegetation index (NDVI), digital elevation model (DEM), terrainslope, aspect, relief and land surface temperatures. Then, the downscaledprecipitation is calibrated by the SRF with rain gauge observations and theaforementioned high-resolution variables. The monthly IntegratedMultisatellitE Retrievals for Global Precipitation Measurement (IMERG) overSichuan Province, China, from 2015 to 2019 was processed using SRF-DC, andits results were compared with those of classical methods includinggeographically weighted regression (GWR), artificial neural network (ANN),random forest (RF), kriging interpolation only on gauge measurements,bilinear interpolation-based downscaling and then SRF-based calibration(Bi-SRF), and SRF-based downscaling and then geographical differenceanalysis (GDA)-based calibration (SRF-GDA). Comparative analyses withrespect to root mean square error (RMSE), mean absolute error (MAE) andcorrelation coefficient (CC) demonstrate that (1) SRF-DC outperforms theclassical methods as well as the original IMERG; (2) the monthly based SRFestimation is slightly more accurate than the annually based SRF fractiondisaggregation method; (3) SRF-based downscaling and calibration performbetter than bilinear downscaling (Bi-SRF) and GDA-based calibration(SRF-GDA); (4) kriging is more accurate than GWR and ANN, whereas itsprecipitation map loses detailed spatial precipitation patterns; and (5)based on the variable-importance rank of the RF, the precipitation interpolatedby kriging on the rain gauge measurements is the most important variable,indicating the significance of incorporating spatial autocorrelation forprecipitation estimation.

Easy-to-use spatial random-forest-based downscaling-calibration method for producing precipitation data with high resolution and high accuracy