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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