Abstract: In recent years, ensemble forecasting has become a vital tool for major global weather forecasting centers. However, ensemble forecasts often exhibit underdispersion and systematic biases, making the application of statistical post-processing methods essential. The standardized anomalies model output statistics (SAMOS) is a commonly used post-processing technique that provides a complete description of the forecast distribution. Nevertheless, SAMOS typically relies only on predictors either directly related to the forecast variable or selected based on subjective judgment, potentially overlooking other valuable predictors. Moreover, directly incorporating too many predictors into SAMOS may lead to overfitting. Therefore, effectively selecting key predictors from numerous variables provided by forecast models remains a significant challenge. Boosting-based variable selection and optimization algorithms have proven to be effective in mitigating overfitting and identifying the most important predictors. This study proposes the standardized anomalies gradient boosting (SABST) method by integrating the strength of SAMOS with a Boosting-based variable selection algorithm. SABST is applied to calibrate ensemble forecasts of 2 m temperature, 2  m relative humidity, and 10  m wind speed. The SABST model is developed using the European Centre for Medium-Range Weather Forecasts (ECMWF) high-resolution ensemble forecast (ensemble prediction system, ENS) products during 2019—2020 and is systematically compared with SAMOS, focusing on its calibration performance and bias correction ability. Results show that compared to ENS and SAMOS, SABST performs better in addressing underdispersion in probabilistic forecasts and improving the accuracy of deterministic forecasts. Based on the continuous ranked probability skill score (CRPSS), SABST improves the average CRPSS by 9.5%, 15.3%, and 4.6% across all forecast lead time compared to SAMOS. These findings demonstrate the advantage of introducing additional potential predictors in the SABST framework and highlight the method's potential for application in ensemble forecast post-processing.