Spatial Downscaling for Global Precipitation Measurement Using a Geographically and Temporally Weighted Regression Model

High-resolution precipitation data are crucial to monitor disasters in urban areas, especially in cases with abundant precipitation. Based on the spatiotemporal, non-stationary relationship between precipitation and normalized differential vegetation index (NDVI), in this paper we introduce a geographically and temporally weighted regression (GTWR) model and further evaluate it in a case study in Guangdong province, China, in the summer of 2015. Our results indicate that there is a mainly negative correlation between precipitation and NDVI in the summer. Our GTWR downscaling model performs better than a previously available geographically weighted regression (GWR) model, providing more accurate downscaled precipitation estimations. This suggests that considering the spatiotemporal, non-stationarity relationship between NDVI and precipitation, our GTWR downscaling model can provide high-spatial resolution precipitation estimates, with more details in urban areas with abundant precipitation.