Development of An Extreme Temperature Forecast Index  from Convection Permitting Ensemble Forecasts

Abundant probability information from ensemble forecasting has been used to forecast extreme weather such as extremely high/low temperatures. In this study, leveraging the Convection Permitting Ensemble Forecasting System (CPEFS) of North China instead of coarse-resolution global ensemble forecast systems as in previous studies, based on the Anderson-Darling test principle, we built an Extreme Weather Forecast Index (EFI) for temperature to forecast extreme temperature events over North China. Using the CPEFS’s three-year historical forecast data, a cumulative distribution function (CDF) for temperature in North China was constructed, establishing a refined model climate capable of identifying extreme temperatures with geographical specific features. The temperature EFI was formulated to reflect differences between the ensemble forecast CDF and the model climate CDF, which can be used to forecast extreme temperatures. We conducted simulation experiments for extremely high (low) temperatures in summer (winter) 2023. The results demonstrate that the EFI warning signals for extreme temperature aligned reasonably with the automatic station observations. It is found that the optimal EFI thresholds are 0.7 or 0.8 (−0.8) for extremely high (low) temperatures. When applying the EFI thresholds to the extreme temperature events in China in 2023, EFI showed a strong skill at the 2-day lead time. However, the forecast accuracy decreased as the forecast lead time extended. Comparison between global ensemble-based EFI and CPEFS-based EFI reveals that high-resolution ensemble-based EFI products could in general achieve a better performance, providing strong warning signals and more refined geographical distributions.