Deep Learning for Seasonal Precipitation Prediction over China

Despite significant progress having been made in recent years, the forecast skill for seasonal precipitation over China remains limited. In this study, a deep-learning-based statistical prediction model for seasonal precipitation over China was developed. The model was trained to learn the distribution of the seasonal precipitation using simultaneous general circulation data. First, it was pre-trained with the hindcasts of several general circulation models (GCMs), and evaluation of the test set suggested that the pre-trained model could basically reproduce the GCM-predicted precipitation, with the anomaly pattern correlation coefficients (PCCs) greater than 0.80. Then, transfer learning was applied by using ECMWF Reanalysis v5 (ERA5) data and gridded precipitation observational data over China, to further correct the systemic errors in the model. As a result, using general circulation fields from reanalysis as the input, this hybrid model performed reasonably well in simulating the seasonal precipitation over China, with the PCC reaching 0.71. In addition, the results using the circulation fields predicted by GCMs as the input were also assessed. In general, the proposed model improves the PCC over China by 0.10-0.13, as compared to the raw GCM outputs, for lead times of 1-4 months. This deep learning model has been used at the National Climate Center of China Meteorological Administration for the past two years to provide guidance for summer precipitation prediction over China and has performed extremely well.