A New Downscaling-Calibration Procedure for TRMM Precipitation Data Over Yangtze River Economic Belt Region Based on a Multivariate Adaptive Regression Spline Model

High-resolution precipitation products are essential for accurate hydrological and meteorological applications. To improve the spatial resolution and accuracy of monthly satellite precipitation products, we developed a new downscaling-calibration framework with three key steps: 1) coarse-resolution satellite precipitation data are downscaled to 1-km resolution precipitation data with multivariate adaptive regression spline (MARS) model; 2) residual correction is applied to bridge the difference between the satellite precipitation data and downscaled precipitation data; and 3) the geographical differential analysis (GDA) calibration method is implemented to improve accuracy by merging the residual-corrected data with rain gauge data. In this study, geolocation variables (longitude and latitude), a digital elevation model (DEM) data, daytime and nighttime land surface temperatures, and four remote sensing indices were used to downscale monthly Tropical Rainfall Measuring Mission (TRMM) 3B43 precipitation datasets over the Yangtze River Economic Belt. The downscaled results showed that the MARS model can avoid “boxy artifact” and pixel-level anomalies, which are often found in geographically weighted regression (GWR) and random forest (RF) results. According to the validation, the step of residual correction is not necessary. With GDA calibration, the MARS-based estimated results were more accurate than the results of the other methods (i.e., GWR and RF) and original TRMM products. Therefore, the developed MARS-based downscaling-calibration procedure can improve not only the spatial resolution but also the quality of the TRMM 3B43 products.