Abstract. Precipitation data with high resolution and high accuracy are
significantly important in numerous hydrological applications. To enhance
the spatial resolution and accuracy of satellite-based precipitation
products, an easy-to-use downscaling-calibration method based on a spatial
random forest (SRF-DC) is proposed in this study, where the spatial
autocorrelation of precipitation measurements between neighboring locations
is considered. SRF-DC consists of two main stages. First, the
satellite-based precipitation is downscaled by the SRF with the incorporation of
high-resolution variables including latitude, longitude, normalized
difference vegetation index (NDVI), digital elevation model (DEM), terrain
slope, aspect, relief and land surface temperatures. Then, the downscaled
precipitation is calibrated by the SRF with rain gauge observations and the
aforementioned high-resolution variables. The monthly Integrated
MultisatellitE Retrievals for Global Precipitation Measurement (IMERG) over
Sichuan Province, China, from 2015 to 2019 was processed using SRF-DC, and
its results were compared with those of classical methods including
geographically 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 difference
analysis (GDA)-based calibration (SRF-GDA). Comparative analyses with
respect to root mean square error (RMSE), mean absolute error (MAE) and
correlation coefficient (CC) demonstrate that (1) SRF-DC outperforms the
classical methods as well as the original IMERG; (2) the monthly based SRF
estimation is slightly more accurate than the annually based SRF fraction
disaggregation method; (3) SRF-based downscaling and calibration perform
better than bilinear downscaling (Bi-SRF) and GDA-based calibration
(SRF-GDA); (4) kriging is more accurate than GWR and ANN, whereas its
precipitation map loses detailed spatial precipitation patterns; and (5)
based on the variable-importance rank of the RF, the precipitation interpolated
by kriging on the rain gauge measurements is the most important variable,
indicating the significance of incorporating spatial autocorrelation for
precipitation estimation.