To enhance the forecasting capability for daily extreme wind speeds, particularly for winds exceeding force 8, this paper uses the “past 3 h gust” wind speed forecast output from the European Centre for Medium-Range Weather Forecasts (ECMWF) model as the primary input factor. Additionally, the paper addresses the extremely uneven sample distribution in the daily extreme wind speed series (samples with wind force above level 8 constitute a very small proportion of the total sample, while samples with wind force below level 5 constitute the vast majority). Moreover, the ECMWF model’s “past 3 h gust” wind speed forecast tends to overestimate low-level winds and underestimate high-level winds. Therefore, the paper leverages nearly five years of surface observations and ECMWF model “past 3 h gust” forecast data to develop a Tabnet-based daily extreme wind classification correction forecast model. The model’s input design includes previous observations, geographic information of the stations, ECMWF forecast fields, and previous forecast error terms. In the evaluation of an independent sample over one and a half years, the new correction forecast model reduces the mean absolute error (MAE) by 45.2% and the root mean square error (RMSE) by 25.7% compared to the interpolated ECMWF model. Furthermore, for wind force levels 1-5 and above 8-9, the new correction forecast model significantly improves the forecasting accuracy compared to the method using interpolated ECMWF forecast fields, demonstrating the feasibility of this forecasting approach.The model is constructed with a focus on overcoming the inherent limitations of the ECMWF model’s wind speed forecasts. By incorporating comprehensive input factors such as historical observation data, the geographical context of observation stations, and systematic forecast error corrections, the model aims to provide a more accurate prediction of extreme wind events. The primary challenge addressed by the model is the skewed distribution of wind force levels in the dataset, where extreme wind events are underrepresented. The innovative use of the Tabnet algorithm allows for a sophisticated analysis and adjustment of the forecast data, thus ensuring higher accuracy in predicting both low and high wind force levels. The independent validation over an extensive period highlights the robustness of the model. The significant reduction in MAE and RMSE underscores the model’s enhanced performance. Specifically, the accuracy improvements for the critical wind force levels 1-5 and 8-9 plus indicate the model’s practical applicability in real-world scenarios. This advancement is crucial for sectors reliant on precise wind forecasts, such as maritime operations, aviation, and disaster preparedness. The results clearly suggest that integrating historical data and addressing the ECMWF model’s biases can lead to substantial improvements in extreme wind speed forecasting. In conclusion, the development of the Tabnet-based correction forecast model represents a significant step forward in meteorological forecasting. By effectively addressing the biases and limitations of existing models, this new approach offers a more reliable tool for predicting extreme wind events.