A methodological approach for spatial downscaling of TRMM precipitation data in North China

Accurate precipitation data with high spatial resolution are crucial for many applications in water and land management. Tropical Rainfall Monitoring Mission (TRMM) data, with accurate, high spatial resolution are crucial for improving our understanding of temporal and spatial variations of precipitation. However, when used in the Three-North Shelter Forest Programme of China, the spatial resolution of TRMM data is too coarse. In this study, we presented a hybrid method, i.e. a regression model with residual correction method, for downscaling annual TRMM 3B43 from 0.25 degrees to 1 km grids from 2000 to 2009. The regression model was applied to construct the relationship among TRMM 3B43 data, continentality (CON), and the normalized difference vegetation index (NDVI) under five different scales (0.25 degrees, 0.50 degrees, 0.75 degrees, 1.00 degrees, and 1.25 degrees). In the residual correction, three spatial interpolation techniques, i.e. inverse distance weighting (IDW), ordinary kriging, and tension spline, were employed. The 1 km monthly precipitation was disaggregated from 1 km annual precipitation by using monthly fractions. Analysis shows that (1) CON was a good variable for precipitation modelling at large-scale regions; (2) the optimum relationship between precipitation, NDVI, and CON was found at a scale of 1.25 degrees; (3) the most feasible option for residual correction was IDW; and (4) the final annual/monthly downscaled precipitation (1 km) not only improved the spatial resolution but also agreed well with data from 220 rain gauge stations (average R-2 = 0.82, slope = 1.09, RRMSE = 18.30%, and RMSE = 51.91 mm for annual downscaled precipitation; average R-2 = 0.41, slope = 0.79, RRMSE = 76.88%, and RMSE = 15.09 mm for monthly downscaled precipitation).