Abstract: Objective forecast of precipitation is difficult because of its complex nonlinear characteristics. In order to enhance the ability of forecasting precipitation, artificial neural network (ANN) method is applied in numerical weather products interpretation. Among different types of ANN, the back propagation (BP) neural network is the most popular and influential one. However, traditional BP algorithm has some limitations such as the difficulties in determining network structure and the learning parameters, poor generalization ability and possibility of misleading to local minimum in learning process, etc. To resolve these problems, an improved algorithm is proposed.Based on T213 numerical forecast products of National Meteorological Center from May to September during 2003—2005, 25 factors are selected in terms of dynamic diagnostic analysis and statistical methods. The precipitation forecasts of operational global models from China National Meteorological Center, Japan Meteorological Agency and German Meteorological Administration are studied. Using the reformative BP algorithm, three grades forecast (≥0.1 mm, ≥10.0 mm, ≥25.0 mm) models are built to forecast 24 hour precipitation of 68 stations over Jiang Huai Basin. During the training process, precipitation samples are randomly divided into two kinds according to a certain proportion, training samples and testing samples. They are used to train the network and to check the error of output respectively so that all parameters are confirmed. By repeating training and learning of network, an optimal network model is obtained. The optimized forecast model is used to forecast precipitation of different grades, times and stations, from May to September during 2006—2007. The forecasting results of improved BP algorithm are compared with those of tradition BP algorithm and numerical models outputs. The average threat score (TS) of improved BP algorithm is the highest; the average false alarm rate (FAR) and missing alarm rate (MAR) of improved BP algorithm are much lower than the others. So the improved BP algorithm is superior and it indicates a potential for more accurate precipitation forecasting.