Advantages of the Multimodel Ensemble Approach for Subseasonal Precipitation Prediction in China and the Driving Factor of the MJO  

作　　者：Li GUO Jie WU Qingquan LI Xiaolong JIA 

机构地区：[1]China Meteorological Administration Key Laboratory for Climate Prediction Studies,National Climate Centre,Beijing 100081,China [2]Collaborative Innovation Centre On Forecast and Evaluation of Meteorological Disasters,Nanjing University of Information Science and Technology,Nanjing 210066,China

出　　处：《Advances in Atmospheric Sciences》2025年第3期551-563,共13页大气科学进展(英文版)

基　　金：sponsored by the National Natural Science Foundation of China(Grant Nos.42175052 and U2442206);the Joint Research Project for Meteorological Capacity Improvement(Grant No.23NLTSQ007,23NLTSZ003);the Innovative Development Special Project of the China Meteorological Administration(Grant No.CXFZ2023J002);the National Key R&D Program of China(Grant No.2023YFC3007700,2024YFC3013100);the China Meteorological Administration Youth Innovation Team(Grant No.CMA2024QN06)。

摘　　要：Based on the hindcasts from five subseasonal-to-seasonal(S2S)models participating in the S2S Prediction Project,this study evaluates the performance of the multimodel ensemble(MME)approach in predicting the subseasonal precipitation anomalies during summer in China and reveals the contributions of possible driving factors.The results suggest that while single-model ensembles(SMEs)exhibit constrained predictive skills within a limited forecast lead time of three pentads,the MME illustrates an enhanced predictive skill at a lead time of up to four pentads,and even six pentads,in southern China.Based on both deterministic and probabilistic verification metrics,the MME consistently outperforms SMEs,with a more evident advantage observed in probabilistic forecasting.The superior performance of the MME is primarily attributable to the increase in ensemble size,and the enhanced model diversity is also a contributing factor.The reliability of probabilistic skill is largely improved due to the increase in ensemble members,while the resolution term does not exhibit consistent improvement.Furthermore,the Madden–Julian Oscillation(MJO)is revealed as the primary driving factor for the successful prediction of summer precipitation in China using the MME.The improvement by the MME is not solely attributable to the enhancement in the inherent predictive capacity of the MJO itself,but derives from its capability in capturing the more realistic relationship between the MJO and subseasonal precipitation anomalies in China.This study establishes a scientific foundation for acknowledging the advantageous predictive capability of the MME approach in subseasonal predictions of summer precipitation in China,and sheds light on further improving S2S predictions.

关 键 词：multimodel ensemble subseasonal predictions summer precipitation S2S model MJO 

分 类 号：P457.6[天文地球—大气科学及气象学] P456.7