Downscaling GCMs using the Smooth Support Vector Machine method to predict daily precipitation in the Hanjiang Basin

General circulation models (GCMs) are often used in assessing the impact of climate change at global and continental scales. However, the climatic factors simulated by GCMs are inconsistent at comparatively smaller scales, such as individual river basins. In this study, a statistical downscaling approach based on the Smooth Support Vector Machine (SSVM) method was constructed to predict daily precipitation of the changed climate in the Hanjiang Basin. NCEP/NCAR reanalysis data were used to establish the statistical relationship between the larger scale climate predictors and observed precipitation. The relationship obtained was used to project future precipitation from two GCMs (CGCM2 and HadCM3) for the A2 emission scenario. The results obtained using SSVM were compared with those from an artificial neural network (ANN). The comparisons showed that SSVM is suitable for conducting climate impact studies as a statistical downscaling tool in this region. The temporal trends projected by SSVM based on the A2 emission scenario for CGCM2 and HadCM3 were for rainfall to decrease during the period 2011-2040 in the upper basin and to increase after 2071 in the whole of Hanjiang Basin.