Effective forecasting of weather events, especially extremes, is critical for minimizing potential damage and ensuring public safety. Yet, ensemble forecasts that allow to represent uncertainty in weather predictions like ECMWF-ENS suffer from biases and dispersion issues. These shortcomings decrease the forecasting skill and introduce the need for statistical post-processing methods like Ensemble Model Output Statistics (EMOS) and Quantile Regression Forest (QRF) to enhance the forecast quality. While these methods increase overall forecasting performance in general, their capability to handle extreme events varies. To strengthen the forecasting of these critical events, a closer examination and potential refinement of the statistical post-processing methods are necessary, along with the development of methods tailored to extreme weather events. This study analyzes the performance of pre-operational post-processing models of ECMWF-ENS wind gust and precipitation forecasts in the Netherlands using EMOS and/or QRF techniques. The skill of the models (using Continuous Probability Ranked Skill Score (CRPSS) and Brier Skill Score (BSS)) for both wind gusts and precipitation is demonstrated. Besides, by focusing on windstorm Poly (July 5th, 2023) and a heavy rain case (June 22nd, 2023), the capabilities of the methods in forecasting specific extreme events are investigated. While the QRF forecasts for the heavy rain case show better skill than for the raw forecasts, for the Poly storm case the post-processed models performed worse than the raw forecasts initially. Thus, this work concentrates on the underlying reasons for the limited performance on that event and proposes an error modeling approach to enhance the post-processing performance of the storm event without compromising the overall forecasting performance.