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.

The traditional model perturbation method of ensemble prediction is usually used to describe random errors of physical processes, but the model inevitably has systematic bias. Therefore, in order to reduce the impact of systematic bias on ensemble prediction, the CMA-GEPS is employed to obtain systematic bias tendency using the empirical orthogonal function (EOF) method. In the integration process, the systematic bias correction method and the traditional Stochastically Perturbed Parameterization Tendency (SPPT) are combined to build a model perturbation method (Bias correction of bias tendency based on SPPT, SPPT-B) that combines systematic bias and random errors of ensemble forecast. Ensemble forecasting experiments are designed and carried out to explore the impact of SPPT-B on global ensemble forecasting. The conclusions are as follow: (1) The first EOF mode of the systematic bias can reflect the main characteristics of the systematic bias well. It shows that basically the systematic bias in the upper troposphere is larger than that in the middle and lower troposphere and increases linearly with forecast lead time. (2) The systematic bias correction method and SPPT-B can effectively reduce the systematic bias in upper and lower levels in the southern and northern Hemispheres and in the tropics, and SPPT-B can significantly improve Spread in the tropics. (3) The effect of the two schemes on the improvement of ensemble prediction skill in the upper troposphere is better than that in the lower troposphere. The above results indicate that the model perturbation method that considers both systematic bias and random errors can effectively improve global ensemble forecasting skill, and can provide a scientific basis for the development of global ensemble forecasting model perturbation method considering both systematic bias and random errors.

Given the chaotic nature of the atmosphere and inevitable initial condition errors, constructing effective initial perturbations (IPs) is crucial for the performance of a convection-allowing ensemble prediction system (CAEPS). The IP growth in the CAEPS is scale- and magnitude-dependent, necessitating the investigation of the impacts of IP scales and magnitudes on CAEPS. Five comparative experiments were conducted by using the China Meteorological Administration Mesoscale Numerical Weather Prediction System (CMA-MESO) 3-km model for 13 heavy rainfall events over eastern China: smaller-scale IPs with doubled magnitudes, larger-, meso-, and smaller-scale IPs; and a chaos seeding experiment as a baseline. First, the constructed IPs outperform unphysical chaos seeding in perturbation growth and ensemble performance. Second, the daily variation of smaller-scale perturbations is more sensitive to convective activity because smaller-scale perturbations during forecasts reach saturation faster than meso- and larger-scale perturbations. Additionally, rapid downscaling cascade that saturates the smallest-scale perturbation within 6 h for larger- and meso-scale IPs is stronger in the lower troposphere and near-surface. After 9–12 h, the disturbance development of large-scale IPs is the largest in each layer on various scales. Moreover, thermodynamic perturbations, concentrated in the lower troposphere and near-surface with meso- and smaller-scale components being dominant, are smaller and more responsive to convective activity than kinematic perturbations, which are concentrated on the middle–upper troposphere and predominantly consist of larger- and meso-scale components. Furthermore, the increasing magnitude of smaller-scale IPs enables only their smaller-scale perturbations in the first 9 h to exceed those of larger- and meso-scale IPs. Third, for forecast of upper-air and surface variables, larger-scale IPs warrant a more reliable and skillful CAEPS. Finally, for precipitation, larger-scale IPs perform best for light rain at all forecast times, whereas meso-scale IPs are optimal for moderate and heavy rains at 6-h forecast time. Increasing magnitude of smaller-scale IPs improves the probability forecast skills for heavy rains during the first 3–6 h.

How to construct appropriate perturbations for convection-permitting ensemble prediction systems (CPEPSs) is a critical issue awaiting urgent solutions. As two common perturbations, initial perturbations (IPs) and lateral boundary perturbations (BPs) interact with each other, affecting the model error growth, especially in mesoscale models. Using the China Meteorological Administration (CMA)-CPEPS, this study tries to elucidate how BPs interact with matched and mismatched IPs under varied large-scale weather conditions/forcings. Seven groups of experiments were conducted for strong-forcing and weak-forcing weather regimes over southern China: three with single IPs, one with single BPs, and three with combined perturbations. It is found that the perturbation magnitudes were dominated by meso-α-scale components, and IPs under weak forcing exhibited more pronounced effects than under strong forcing; whereas BPs exerted more pronounced effects under strong forcing than weak forcing regimes. Furthermore, it lasts longer for high-level variables when the perturbation energy from BPs is higher than that from IPs, compared to low-level variables. Moreover, for precipitation and dynamic variables, IPs and BPs can mutually reinforce. The source of these perturbations, and their specific vertical levels, do not alter the extent of their interactions. Nevertheless, the weather regime and the scales of the perturbations influence the strength of their mutual reinforcement. In particular, the weak-forcing regimes exhibit a more pronounced reinforcing effect, and meso-α-scale perturbations are more conducive to fostering interactions compared to meso-β-scale ones. Ultimately, it is the perturbation magnitude inherent in the initial perturbation itself that determines the interactions between IPs and BPs.