Here,a CNN-Attention-BP model is proposed for precipitation forecast based on analysis of the characteristics of precipitation statistical prediction models,then empirical analyses are made on the summer rainfall in Changchun,Baicheng and Yanji stations of Jilin province for the period of 1961-2020.First,a Convolution Neural Network (CNN) is used to study the characteristics of precipitation,air pressure,wind speed,air temperature and relative humidity.Second,the attention mechanism is used to determine the weight of meteorological factors for precipitation forecast.Then a BP neural network is applied to predict the precipitation probability.And the performance of the proposed CNN-Attention-BP model is evaluated by accuracy,cross-entropy loss function and F1-score,which is compared with that of the support vector machine,multi-layer perceptron and convolution neural network model.The results show that the CNN-Attention-BP model is characterized by autonomous learning and paying more attention to significant information,as well as the improved forecasting performance in summer precipitation occurrence for Jilin province.Meanwhile,the proposed model would perform better with more balanced sample and precipitation frequency closer to 0.5,when accuracy would reach up to 88.4％.Compared with single models,the CNN-Attention-BP improves the forecast accuracy by 17 percentage points.