Advantages of the Multimodel Ensemble Approach forSubseasonal Precipitation Prediction in Chinaand the Driving Factor of the MJO

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.