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

Under global warming, substantial changes have occurred in the intensity and frequency of extreme heat events in China, and accurate prediction is critical to reducing damages from the extreme weather-induced disasters. 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 surface air high temperature at the 8-day lead in China with a good performance on the subseasonal timescale. Through designing a new verification approach of normalization, we show that the EFI of China high temperature has skills at 1–19-day leads, and as lead time extends, the Threat Score (TS) and the Factions Skill Score (FSS) decline. In addition, the threshold of extreme temperature has been identified by using such a normalization approach; i.e., an observed dimensionless anomaly value of around 1.28 corresponds to the 90th percentile of temperature for defining 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.