A Machine Learning-Based Observational Constraint Correction Method for Seasonal Precipitation Prediction

Seasonal precipitation has always been a key focus of climate prediction. As a dynamic-statistical combined method, the existing observational constraint correction establishes a regression relationship between the numerical model outputs and historical observations, which can partly predict seasonal precipitation. However, solving a nonlinear problem through linear regression is significantly biased. This study implements a nonlinear optimization of an existing observational constrained correction model using a Light Gradient Boosting Machine (LightGBM) machine learning algorithm based on output from the Beijing National Climate Center Climate System Model (BCC-CSM) and station observations to improve the prediction of summer precipitation in China. The model was trained using a rolling approach, and LightGBM outperformed Linear Regression (LR), Extreme Gradient Boosting (XGBoost), and Categorical Boosting (CatBoost). Using parameter tuning to optimize the machine learning model and predict future summer precipitation using eight different predictors in BCC-CSM, the mean Anomaly Correlation Coefficient (ACC) score in the 2019–22 summer precipitation predictions was 0.17, and the mean Prediction Score (PS) reached 74. The PS score was improved by 7.87% and 6.63% compared with the BCC-CSM and the linear observational constraint approach, respectively. The observational constraint correction prediction strategy with LightGBM significantly and stably improved the prediction of summer precipitation in China compared to the previous linear observational constraint solution, providing a reference for flood control and drought relief during the flood season (summer) in China.