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 forecast accuracy at the medium-to-long range. In 2020, the Key Influential Systems based Analog Model (KISAM) developed in National Meteorological Center of China was further improved and brought into operational application, and its skill in forecasting heavy precipitation events (HPEs) of both long and short durations is analyzed in this study. Verification and comparison of this newly adapted analog model against the ECMWF ensemble mean forecasts at lead times of up to 15 days are carried out for the identified 16 HPEs over 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. However, at short lead times, ECMWF ensemble mean is advantageous over KISAM due largely to the low false alarm rates (FARs) benefited from an underestimation of the frequency of heavy precipitation. Analysis revealed that at the medium-to-long forecast range, the large fraction of misses induced by the high degree of under forecasting overwhelms the fairly good FARs in the ECMWF ensemble mean, which partly explains its inferiority to KISAM in terms of 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.