Abstract: Extreme Forecast Index (EFI) provides an effective tool to extract extreme weather information from ensemble forecasts. To improve the ability of the CMA global ensemble prediction system (CMA-GEPS) for extreme weather forecast and address the difficulty of reasonably calculating the model climate distribution due to small samples of historical forecasts by CMA-GEPS and the lack of re-forecast data, this study develops a method to build the model climate distribution required by EFI using insufficient samples of deterministic forecasts. Based on the CMA global high-resolution (0.25°×0.25°) deterministic operational forecast data from 15 June 2020 to 22 July 2022, the model climate distributions are constructed for each month at different forecast lead times (1—10 d) that match the lower-resolution (0.5°×0.5°) CMA-GEPS forecast model version through extending the forecast samples in both time and space. By employing the operational forecast data of CMA-GEPS and the ERA5 reanalysis data, the forecast ability of CMA-GEPS for extreme high temperature in four representative regions both domestic and abroad for the summer of 2022 (June to August) is evaluated. Results from the relative operating characteristic curve show that the CMA-GEPS EFI has the ability to detect extreme high temperature within the short- and medium-range forecast lead times of 1—10 d. Taking the maximum TS score as the criterion, the critical threshold of EFI for issuing warning signals of extreme high temperature is determined. The forecast ability of EFI decreases with increasing forecast lead time, and different performances exhibit in different regions: the forecast ability for extreme high temperature in the middle and lower reaches of the Yangtze river in China is higher than that in North China for all lead times; the forecast ability of EFI in western Europe is better than that in central Europe for the 1—7 d lead times, yet the EFI forecast ability in central Europe for the 8—10 d lead times is better. Above results are related to the variation of ensemble forecast quality of 2 m temperature with forecast lead time and spatial location. Evaluation results from the economic value model reveal that risk decisions based on the EFI forecast information demonstrate certain economic values and reference values. Analysis results from a case study further indicate that the CMA-GEPS EFI can provide early warnings of extreme high temperature in the medium forecast range.