Based on the summer daily precipitation data of 60 meteorological stations in Anhui province from 1999 to 2009 and observation data of Anqing sounding station,the relationship between local precipitation and large-scale precipitation circulation in different mid-low wind directions are studied in this paper.The neural network and linear regression method,combined with 3 forecasting objects and corresponding predictor variables are employed to design 6 downscaling function models to approximate and optimize the precipitation data.The 6 models are used to simulate and forecast the daily precipitation data of 46 meteorological stations in Anhui province,and the results show that BP neural network models generally outperform the linear regression models in simulation and forecasting accuracy,indicating the nonlinear correlation between different scales of summer rainfall.Compared with the commonly used Cressman interpolation methods,the neural network models can reflect the basic distribution and local characteristics of summer precipitation in forecasting results.The BP neural network model with single station precipitation series as prediction object has good forecasting results in areas of plains or rivers,while the BP neural network model with the REOF principal components as predicting object is good in mountainous area.