Key Points

• The Subseasonal-to-Seasonal prediction of summer precipitation over southern China is improved with a U-Net based deep learning method

• The U-Net demonstrated promising performance in both general statistics and extreme events and shows superiority to the quantile mapping benchmark

• The model skills arise from precipitation itself at the early stage, while atmospheric factors play important roles at longer lead times