Medium-Extended Range Forecasting of Meiyu in 2023: Performance of CMA and ECMWF GEPSs

Meiyu, featuring prolonged periods of rainfall over the Yangtze-Huai River Basin (YHRB), not only replenishes water resources and sustains ecological balance, but also poses potential disaster risks. Accurate early forecasting of Meiyu is crucial for effectively implementing flood prevention strategies. To help refining numerical models and providing guidance for operational forecasters, this study explores the capabilities of two global ensemble prediction systems (GEPSs) of the CMA and the ECMWF in forecasting the Meiyu characteristics in 2023 over the YHRB. Results show that the ECMWF GEPS reasonably forecasts the Meiyu rainfall, while the CMA GEPS presents a notable underestimation. The predictable lead time of the Meiyu onset date is eight days by the ECMWF GEPS and six days by the CMA GEPS, respectively. Regarding the regional rainstorm processes, the two GEPSs generally provide a predictable lead time of 48–168 hours for reasonably forecasting the pattern of the heavy rainfall area. To further examine their strengths and weaknesses in Meiyu forecasting, this paper revisits their abilities in forecasting key circulation systems. By verifying against their respective analyses, it is demonstrated that the ECMWF GEPS reasonably forecasts the spatial coverages of the northwestern Pacific subtropical high (NWPSH) and the South Asian high (SAH), whereas the CMA GEPS presents substantial underestimation. Both GEPSs show generally southward deviations for the eastern ridge line position (RLP) of the SAH, and exhibit northward deviation for the western RLP of the NWPSH during early forecast lead times. The less Meiyu rainfall predicted by the CMA GEPS compared to the ECMWF GEPS can be attributed to its weaker low-level convergence belt and weaker upper-level divergence area. A deeper exploration into these forecast discrepancies in upper-level divergence and lower-level convergence suggests that they likely originate from their initial analysis fields.