Based on the ERA-40 daily reanalysis data from 1961-2002 and observed daily precipitation data at 56 meteorological stations located in the Yangtze-Huaihe River basin, this study applies a new downscaling method based on Self-Organizing Maps (SOMs) to produce downscaled summer precipitation estimates at each station. The simulation capability of the statistical downscaling approach for monsoon precipitation and extreme precipitation over East China have been assessed. The downscaling model is then applied to simulate daily precipitation at the same 56 stations for the period 1986-2005 using predictor sets simulated by BCC-CSM1.1(m) (Climate System Model of the Beijing Climate Center). Results show that the downscaling approach can realistically reproduce the observed probability distribution and temporal variability of precipitation. The Brier scores are almost zero and the significance scores are above 0.8 for all stations. Average biases of the downscaled number of days with precipitation greater than 1 mm and 10 mm, the summer total precipitation, the simple daily intensity index, the extreme daily precipitation threshold, and the fraction of total precipitation due to events exceeding the 95th percentile of the climatological wet-day precipitation distribution all are below 11%. Furthermore, the downscaling approach is, to a certain extent, able to reproduce the temporal variability characteristics of precipitation. Compared with that for the raw outputs of BCC-CSM1.1(m), the biases of the above indices for the downscaled results reduce by 40% to 60%. The spatial correlation coefficients increase to 0.9, and the root mean square errors are below 0.5. Overall, the downscaling model significantly improves the simulation of the probability distribution of daily precipitation, particularly the simulation of extreme precipitation. Thereby it can be applied for the projection of future precipitation changes.