Statistical downscaling methods are usually used to fill the gap between large-scale climate change data and fine-scale hydrological application. Automated Statistical Downscaling (ASD) technique was investigated with the combination of a case study in 3 typical basins of eastern monsoon region in China in this paper. Statistical relationship between the predictant and predictor was developed by using the observed data of the 3 river basins and ERA-40 reanalysis data, and the high resolution daily precipitation, daily air temperature, daily maximum temperature and daily minimum temperature were then generated. Finally, the performance and suitability of ASD model were evaluated. The results show that ASD is capable of simulating temporal and spatial change characteristics of data se - ries. Furthermore, the efficiency of simulation on air temperature variables is better than precipitation, and the performance of this model changes in different basins, as depending on geographic and meteorological factors of the study area. In a word, ASD model performs effec - tively in the study areas, which can be used to generate future scenarios in the related studies