In recent years, the development of machine learning, especially deep learning, has provided new methods and ideas for current climate research. Stacked auto-encoder based on deep learning are used to perform nonlinear down-scaling to compress the degree of freedom of climate variables in the early stage. And climate predictors features are extracted from the summer precipitation in four regions in eastern China, from which key climate predictors affecting summer precipitation in each region are identified. On this basis, a variety of regression methods including random forest in machine learning method were used to construct prediction models for key climate predictors in each region. The best model parameters were determined by the sensitivity test of model parameters to forecast results. Several years of predictions suggest that the method for the forecast of precipitation in eastern China has a very high skill, especially in southern China. The results show that the anomaly consistency of the proposed model in regional prediction is better than that of the model. Compared with the mainstream model, the prediction results in South China can be improved by more than 10%. The method has a good application prospect for summer precipitation prediction in eastern China.