A Bias Correction Approach for Extreme Heat Event Forecasts over Complex Terrain

With the intensification of global warming, the Sichuan-Chongqing region of China has experienced a growing incidence of heat waves and associated disasters. However, due to complex topographic conditions, model-based early warning capabilities remain inadequate, particularly on the sub-seasonal to seasonal (S2S) scale. In this study, we analyze the prediction skills of five S2S models (CMA, ECCC, ECMWF, NCEP and UKMO). Additionally, focusing on models that demonstrate superior skills in forecasting summer daily maximum air temperature (Tmax), we develop a set of bias correction approaches and conduct a comprehensive evaluation on them. Results indicate that the CMA performs best overall, particularly in predicting heat events, while the ECMWF shows better capability to capture the temporal variability of daily Tmax among the five models. Forecast errors of both models exhibit notable sensitivity to terrain features and are correlated with El Niño-Southern Oscillation (ENSO) variations. The quantile mapping bias correction method that integrates complex topography, ENSO phases, and model forecast attributes reliably improves the forecast skill of both CMA and ECMWF in the SCR, whether in hindcast or independent validation. It can help ECMWF to successfully capture heat events that were widely missed in the original forecasts. Furthermore, it delivers effective improvement in forecast accuracy of the two models for extreme heat waves in August 2006 and 2022.