Subseasonal Extreme Forecast Index for High Temperature Based on the ECMWF S2S Model Data

Under the global warming, substantial changes have occurred in the intensity and frequency of extreme heat events in China, and accurate prediction of such events is critical to disaster prevention and mitigation. Extreme forecast index (EFI), as an effective method, has been widely used in extreme weather short-range prediction research and operation. This study applies the EFI method to the subseasonal forecasts of summer extreme high temperature in China based on the subseasonal-to-seasonal (S2S) model data from the ECMWF during 2015–2023. The results show that the EFI method can provide skillful predictions of extreme high temperature of surface air at an 8-day lead time in China, with good performance on the subseasonal timescale. Through a new verification formula using normalization, we show that the EFI of high temperature in China has decent prediction skills at 1–19-day lead times, and as lead time extends, the Threat Score (TS) and Factions Skill Score (FSS) decline. In addition, the threshold of extreme temperature has been identified by using a similar normalization method; that is, an observed dimensionless temperature anomaly value of around 1.28, which corresponds to the 90th percentile of the temperature data, can be recognized as an extreme high temperature. The effectiveness of the EFI verification and threshold identification approaches shed lights on the improvement for subseasonal prediction of extreme high temperature in China.