Statistical downscaling of daily precipitation using support vector machines and multivariate analysis

Downscaling local daily precipitation from large-scale weather variables is often necessary when studying how climate change impacts hydrology. This study proposes a two-step statistical downscaling method for projection of daily precipitation. The first step is classification to determine whether the day is dry or wet, and the second is regression to estimate the amount of precipitation conditional on the occurrence of a wet day. Predictors of classification and regression models are selected from large-scale weather variables in NECP reanalysis data based on statistical tests. The proposed statistical downscaling method is developed according to two methodologies. One methodology is support vector machine (SVM), including support vector classification (SVC) and support vector regression (SVR), and the other is multivariate analysis, including discriminant analysis (for classification) and multiple regression. The popular statistical downscaling model (SDSM) is analyzed for comparison. A comparison of downscaling results in the Shih-Men Reservoir basin in Taiwan reveals that overall, the SVM reproduces most reasonable daily precipitation properties, although the SDMS performs better than other models in small daily precipitation (less than about 10 mm). Finally, projection of local daily precipitation is performed, and future work to advance the downscaling method is proposed.