At present,precipitation forecasting mainly relies on numerical weather forecasting models.However,due to factors such as physical parameterization and computational resources,there remains significant uncertainty in precipitation forecasting based on numerical models.In recent years,deep learning has shown great advantages and potential in the field of weather forecasting.The present study constructs neural networks to predict daily precipitation distribution in the northeastern United States,to explore the capabilities of neural-network models in predicting high-resolution precipitation (CPC,0.25°) using low-resolution meteorological fields (ERA-Interim,0.7°).Next,the study compares the performance of three mainstream network frameworks (VGG,ResNet,and GoogleNet) in the aforementioned task.The results indicate that all three frameworks have certain capabilities for predicting the daily precipitation distribution in the northeastern United States,with VGG performing the best,but their root mean square error (RMSE) is higher than that of the ERA-Interim 24-hour (ERA24) prediction.The ensemble-mean results of the three neural networks are all superior to the ERA24 prediction,and combining these three with the ERA24 prediction results can significantly improve ERA24 prediction in different seasons and intensities.It is thus concluded that deep learning has great potential in improving the resolution and accuracy of precipitation prediction.