Winter Temperature Prediction in Northeast China: The XGBoost-Based Cold/Warm Year Ensemble Model

Abstract Winter temperature in Northeast China significantly affects agriculture, energy use, and daily life, making accurate predictions is vital for social and economic planning. Prediction skills of the dynamical model for winter temperature prediction are different between warm and cold years, which has not been fully considered in previous multimodel ensemble predictions. Extreme gradient boosting (XGBoost), one of the popular deep learning methods, can use the gradient boosting to effectively handle large datasets and make it adept at capturing nonlinear temperature dynamics. This study utilizes three dynamical model predictions, along with on-site observation, and develops an XGBoost-Based Cold/Warm Year Ensemble Model (XEM). Validation results indicate that the XEM model shows different performances in winter temperature prediction among dynamical models. XEM can effectively improve accuracy and predictive skill of the winter temperature prediction comparing with the original model in Northeast China, with the average increase of anomaly correlation coefficient (ACC) by 0.49 and anomaly consistency score (PCS) by 21.1%. The XEM demonstrated remarkable enhancements in the independent sample validation in 2021 and 2022. The ACC achieved its maximum improvement of 0.85, while the PCS saw its highest enhancement at 45.9%, indicating a substantial leap in predictive performance. Additionally, the Shapley additive explanation (SHAP) value and gain analysis reveals that XEM can properly prioritize meteorological factors like 200-hPa geopotential height anomalies (H200) and 2-m air temperature (T2m) in its ensemble prediction, which provides robust insights on why XEM can effectively improve the winter predictions. Significance Statement This study introduces a pioneering extreme gradient boosting (XGBoost)-Based Cold/Warm Year Ensembled Model (XEM) model for predicting winter temperatures in Northeast China. By modeling cold and warm years separately and identifying key meteorological factors [such as H200 and zonal wind at 200 hPa (u200)], the model enhances prediction accuracy. It classifies the prediction year’s temperature attribute and ensembles results through weighted averaging. The approach achieves significant improvements in independent validation for 2021 and 2022, offering a robust and adaptable method for temperature prediction that could inform climate variability strategies in other regions.