Long-Term Forecast of Heatdays and Heatwaves Incidents in Temperate Continental Climate Zone of China

The degree of harm caused by HD and HW resulting from global warming varies across different climatic regions, making accurate forecasting crucial for these regions. This paper is based on the output data of the Global Ensemble Forecast System and analyzes the performance improvement of forecasting long-term HD and HW at 12 stations in China's temperate continental climate zone using the Equal Distance Cumulative Distribution Function Matching (EDCDFm) method, with Inverse Distance Weighting (IDW) used as the control. The EDCDFm method produced mean Root Mean Square Error (RMSE) of 3.94 ℃, Coefficient of Determination (R2) of 0.92, and bias (IBAS) of 0.037 ℃ for the HD forecast, while the IDW method had RMSE of 6.46 ℃, R2 of 0.91, and bias (BIAS) of −0.241 ℃. Notably, the average Probability of Detection (POD) of EDCDFm for HD was 69.72%, eight days in advance of the event, while the POD of IDW was only 40.60%, with a corresponding eight-day-ahead POD of 42.54%. The EDCDFm improved the forecast performance by 71.72 and 83.52% compared to IDW for HD. For the HWN forecast, the average POD of EDCDFm was 76.61%, with an eight-day-ahead POD of 89.67%, while the IDW method had an average POD of 43.52%, with an eight-day-ahead POD of 49.38%. The forecast performance of EDCDFm was improved by 76.03 and 81.59% compared to IDW for HWN. In conclusion, using EDCDFm to forecast HD and HW in regional climate is effective, and can provide decision-makers with accurate forecasting information.