A Dynamical and Machine Learning Hybrid Seasonal Prediction of Summer Rainfall in China

The seasonal prediction of summer rainfall is crucial for regional disasters reduction, but currently has a low prediction skill. We developed a dynamical and machine learning hybrid (MLD) seasonal prediction method for summer rainfall in China based on circulation fields from the CAS FGOALS-f2(无全称) operational dynamical prediction model. By selecting the optimum hyperparameters for three machine learning methods to obtain the best fit and least overfitting, an ensemble mean of the random forest and gradient boosting regression tree methodswas shown to have the highest prediction skill measured by the anomalous correlation coefficient. The skill had anaverage value of 0.34 in the historical cross-validation period (1981–2010) and 0.20 in a 10-yr period (2011–2020) of independent prediction, which significantly improves the dynamical prediction skill by 400%. Both reducing overfitting and using the best dynamical prediction are important in applications of the dynamical and MLD method and thisrequires further investigation.