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 increases 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.

Extreme Forecast Index (EFI) provides an effective tool to extract extreme weather information from ensemble forecasts. To improve the ability of the CMA global ensemble prediction system (CMA-GEPS) for extreme weather forecast and address the difficulty of reasonably calculating the model climate distribution due to small samples of historical forecasts by CMA-GEPS and the lack of re-forecast data, this study develops a method to build the model climate distribution required by EFI using insufficient samples of deterministic forecasts. Based on the CMA global high-resolution (0.25°×0.25°) deterministic operational forecast data from 15 June 2020 to 22 July 2022, the model climate distributions are constructed for each month at different forecast lead times (1—10 d) that match the lower-resolution (0.5°×0.5°) CMA-GEPS forecast model version through extending the forecast samples in both time and space. By employing the operational forecast data of CMA-GEPS and the ERA5 reanalysis data, the forecast ability of CMA-GEPS for extreme high temperature in four representative regions both domestic and abroad for the summer of 2022 (June to August) is evaluated. Results from the relative operating characteristic curve show that the CMA-GEPS EFI has the ability to detect extreme high temperature within the short- and medium-range forecast lead times of 1—10 d. Taking the maximum TS score as the criterion, the critical threshold of EFI for issuing warning signals of extreme high temperature is determined. The forecast ability of EFI decreases with increasing forecast lead time, and different performances exhibit in different regions: the forecast ability for extreme high temperature in the middle and lower reaches of the Yangtze river in China is higher than that in North China for all lead times; the forecast ability of EFI in western Europe is better than that in central Europe for the 1—7 d lead times, yet the EFI forecast ability in central Europe for the 8—10 d lead times is better. Above results are related to the variation of ensemble forecast quality of 2 m temperature with forecast lead time and spatial location. Evaluation results from the economic value model reveal that risk decisions based on the EFI forecast information demonstrate certain economic values and reference values. Analysis results from a case study further indicate that the CMA-GEPS EFI can provide early warnings of extreme high temperature in the medium forecast range.

Using the 500 hPa geopotential height (H₅₀₀) forecast data of the CMA global ensemble prediction operational system (CMA-GEPS) for a 1-year period from 1 June 2020 to 31 May 2021, the scale-dependent characteristics of error growth and forecast performance of the CMA-GEPS in the Northern Hemisphere are evaluated. The H₅₀₀ field is decomposed into different scales (including the planetary scale, the synoptic scale and the sub-synoptic scale) by using the spectral filtering method. The relationship between the Root Mean Square Error of the ensemble mean (RMSE) and ensemble spread (SPD) indicates that the CMA-GEPS is over-dispersive (RMSE is smaller than SPD) in the early forecast stage (before 108 h), which is mainly attributed to the excessive dispersion on the synoptic scale. In the subsequent forecast period (beyond 108 h), the CMA-GEPS is under-dispersive (RMSE is greater than SPD), which is caused by insufficient spread on both the planetary scale and the synoptic scale. The error growth model modified by Dalcher et al. in 1987 is applied to diagnose the characteristics of the H₅₀₀ forecast error growth. It is found that the error growth processes of CMA-GEPS are reasonable, and the initial error grows fastest on the sub-synoptic scale and slowest on the planetary scale. In terms of absolute (relative) errors, impacts of model errors on forecast errors increase (decrease) with increasing spatial scale. In addition, taking the climatological distribution derived from the daily dataset of the ERA-Interim reanalysis for 30 years from 1989 to 2018 as the reference forecast, the Continuously Ranked Probability Skill Score (CRPSS) is computed to verify the probabilistic forecast skills of H₅₀₀ within CMA-GEPS together with its components on different scales. Results reveal that the forecast skills for the planetary scale are the highest, and those for the sub-synoptic scale are the lowest. Moreover, the probabilistic skills of the unfiltered H₅₀₀ lie between skills of the planetary and synoptic scales. Above diagnostic results can provide an objective basis for further improvement of the CMA-GEPS.