Abstract: Traditional ensemble forecasts are based on deterministic models, and their performance is directly affected by the forecast quality of the deterministic models they rely on. The China Meteorological Administration Global Forecast System (CMA-GFS) was upgraded from V3.3 to V4.0 in 2023, resulting in significant improvements in both its horizontal resolution and forecast performance. This upgrade also provides a crucial foundation for optimizing the CMA Global Ensemble Prediction System (CMA-GEPS) with the CMA-GFS as the forecast model. To investigate the impact of the forecast model update on CMA-GEPS forecasts in wintertime, the operational CMA-GEPS V1.3 is taken as the baseline in this paper, and two ensemble forecast experimental systems are constructed using CMA-GFS V3.3 and V4.0, respectively, with the only difference lying in the forecast model. Two groups of 31 d ensemble forecast comparative experiments for the winter season of 2024 are carried out, and analyses are implemented from three perspectives, i.e., perturbation characteristics, statistical scores, and a representative case study. Results show that after the forecast model is updated from CMA-GFS V3.3 to V4.0, the ability of ensemble perturbations from CMA-GEPS to capture the forecast errors is slightly improved, and the perturbations grow faster. Moreover, the ensemble spread (SPD) of most elements verified increase significantly. Except that the ensemble mean Root-Mean-Square Errors (RMSE) of upper-level temperature in the tropical region deteriorate, the RMSEs of other variables remain nearly unchanged or improved. Overall, the gap between SPD and the ensemble mean RMSE becomes narrow, suggesting an enhanced ensemble reliability. Apart from the upper-level temperature in the tropical region, both the Continuous Ranked Probability Score (CRPS) and Outlier primarily decline, indicating improved probabilistic forecast skills. Over China, the forecast skill of light rain is nearly unchanged, and forecasts of moderate rain and heavy rain are improved to some extent. In summary, the forecast model upgrade generally improves the wintertime forecast performance of CMA-GEPS, which is further confirmed through the analysis of a representative cooling weather process in the northeastern region of China in late November. However, the increased SPD introduced by the new forecast model exacerbates the over-dispersive problem of the geopotential height forecast from CMA-GEPS during the early forecast period. Therefore, even if the initial perturbation technology, model perturbation strategy, horizontal resolution, and ensemble size of the ensemble prediction system are kept unchanged, it is still necessary to optimize the perturbation parameters after updating the forecast model to achieve a comprehensive improvement of the CMA ensemble forecast skill. In the future, impacts of the forecast model upgrade on CMA-GEPS in other seasons will be further explored.