Deterministic and Probabilistic Evaluation of Sub-Seasonal Precipitation Forecasts at Various Spatiotemporal Scales over China during the Boreal Summer Monsoon

Skillful sub-seasonal precipitation forecasts can provide valuable information for both flood and drought disaster mitigations. This study evaluates both deterministic and probabilistic sub-seasonal precipitation forecasts of ECMWF, ECCC, and UKMO models derived from the Sub-seasonal to Seasonal (S2S) Database at various spatiotemporal scales over China during the boreal summer monsoon. The Multi-Source Weighted-Ensemble Precipitation, version 2 (MSWEP V2), is used as the reference dataset to evaluate the forecast skills of the models. The results suggest that skillful deterministic sub-seasonal precipitation forecasts are found when the lead time is within 2 weeks. The deterministic forecast skills reduce quickly when the lead time is beyond 2 weeks. Positive ranked probability skill scores (RPSS) are only found when the lead time is within 2 weeks for probabilistic forecasts as well. Multimodel ensembling helps to improve forecast skills by removing large negative skill scores in northwestern China. The forecast skills are also improved at larger spatial scales or longer temporal scales. However, the improvement is only observed for certain regions where the predictable low frequency signals remain at longer lead times. The composite analysis suggests that both the El Niño–Southern Oscillation (ENSO) and Madden–Julian Oscillation (MJO) have an impact on weekly precipitation variability over China. The forecast skills are found to be enhanced during active ENSO and MJO phases. In particular, the forecast skills are found to be enhanced during active MJO phases.