Post-processing sub-seasonal precipitation forecasts at various spatiotemporal scales across China during boreal summer monsoon

Post-processing outputs from Coupled Global Circulation Models (CGCM) is required to generate skillful and reliable sub-seasonal precipitation forecasts. However, it is not currently known at what spatiotemporal scale can sub-seasonal precipitation forecasts be improved after post-processing. In this study, a Bayesian joint probability (BJP) method is used to post-process European Centre for Medium-Range Weather Forecasts (ECMWF) sub-seasonal precipitation forecasts at various spatiotemporal scales across China during the boreal summer monsoon. The relationships between forecasts and observations are built at daily, weekly, and fortnight temporal scales for each grid cell and each hydroclimatic region after normalization. The results suggest that the skills and reliability of ECMWF raw daily precipitation forecasts are improved after post-processing. The forecast skills are further improved at larger spatial scales or longer temporal scales, especially in the Upper and Middle Yangtze River, Southeast Rivers, and Pearl River. Although the forecast skills decrease rapidly as lead time increases, the uncertainty spread of the BJP calibrated sub-seasonal precipitation forecasts is highly reliable at all lead times and spatiotemporal scales. This work will help to realize the potential of CGCM forecasts at sub-seasonal time scales. Future works will investigate the benefits of post-processing precipitation forecasts for sub-seasonal streamflow predictions.

Automated summary

Post-processing of sub-seasonal precipitation forecasts from ECMWF using Bayesian joint probability method enhances forecast skills, especially at larger spatial and longer temporal scales. Although forecast skills decrease with lead time, the uncertainty spread of the post-processed forecasts remains reliable across all lead times and spatiotemporal scales.