Improving subseasonal forecasting of East Asian monsoon precipitation with deep learning

Accurate subseasonal forecasting of East Asian summer monsoon (EASM) precipitation is crucial, as it directly impacts the livelihoods of billions. However, the prediction skill of state-of-the-art subseasonal-to-seasonal (S2S) models for precipitation remains limited. In this study, the authors developed a convolutional neural network (CNN) regression model to enhance the prediction skill for weekly EASM precipitation by utilizing the more reliably predicted circulation fields from dynamic models. The outcomes of the CNN model are promising, as it led to a 14 % increase in the anomaly correlation coefficient (ACC), from 0.30 to 0.35, and a 22 % reduction in the root-mean-square error (RMSE), from 3.22 to 2.52, for predicting the weekly EASM precipitation index at a leading time of one week. Among the S2S models, the improvement in prediction skill through CNN correction depends on the model's performance in accurately predicting circulation fields. The CNN correction of EASM precipitation index can only rectify the systematic errors of the model and is independent of whether the each grid point or the entire area-averaged index is corrected. Furthermore, u200 (200-hPa zonal wind) is identified as the most important variable for efficient correction.