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Monthly extreme precipitation (EP) forecasts are of vital importance in water resources management and storage behind dams. Machine learning (ML) is extensively used for forecasting monthly EP, and improvements in model performance have been a popular issue. The innovation of this study is summarized as follows. First, a distance correlation-Pearson correlation (DC-PC) method was proposed to identify the complex nonlinear relationship between global sea surface temperature (SST) and EP and select key input factors from SST. Second, a random forest (RF) model was used for forecasting monthly EP, and the physical mechanism of EP was obtained based on the feature importance (FI) of RF and DC–PC relationship. The middle and lower reaches of the Yangtze River (MLYR) were selected as a case study, and monthly EP in summer (June, July and August) was forecasted. Furthermore, the physical mechanism between key predictors with a large proportion of FI and EP was investigated. Results showed that the proposed model had high accuracy and robustness, in which R2 in the test period was above 0.81, and RMSE as well as MAE were below 10 mm. Meanwhile, the key predictors in the high SST years could cause eastward extension of the South Asian High, westward extension of the Western Pacific Subtropical High, water vapor rising motion and an increase in the duration of atmospheric rivers exceeding 66 h, which lead to increasing EP in the MLYR. The results indicated that the DC–PC method could replace Pearson correlation for investigating the nonlinear relationship between SST and EP, as well as for selecting the factors. Further, the key predictors that account for a large proportion of FI can be used for explaining the physical mechanism of EP and directing forecasts.

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