Application of an Improved Analog-Based Heavy Precipitation Forecast Model to the Yangtze–Huai River Valley and Its Performance in June–July 2020

Precipitation extremes, such as the record-breaking Meiyu characterized by frequent occurrences of rainstorms that resulted in severe flooding over the Yangtze–Huai River valley (YHRV) in June–July 2020, are always attracting considerable interest, highlighting the importance of improving the forecast accuracy at the medium-to-long range. To elevate the skill in forecasting heavy precipitation events (HPEs) with both long and short durations, the Key Influential Systems Based Analog Model (KISAM) was further improved and brought into operational application in 2020. Verification and comparison of this newly adapted analog model and ensemble mean forecasts from the EC-MWF at lead times of up to 15 days were carried out for the identified 16 HPEs over the YHRV in June–July 2020. The results demonstrate that KISAM is advantageous over ECMWF ensemble mean for forecasts of heavy precipitation ≥ 25 mm day⁻¹ at the medium-to-long (6–15-day) lead times, based on the traditional dichotomous metrics. At short lead times, ECMWF ensemble mean outperforms KISAM due largely to the low false alarm rates (FARs) benefited from an underestimation of the frequency of heavy precipitation. However, at the medium-to-long forecast range, the large fraction of misses induced by the high degree of underforecasting overwhelms the fairly good FARs in the ECMWF ensemble mean, which partly explains its inferiority to KISAM in terms of the threat score. Further assessment on forecasts of the latitudinal location of accumulated heavy precipitation indicates that smaller displacement errors also account for a part of the better performance of KISAM at lead times of 8–12 days.