Diagnostic Evaluation of Spread–Skill Relationships for Convection‐Permitting Ensemble Prediction System

Accurate representation of forecast uncertainty is essential for effective ensemble forecasting. To quantify the strengths and weaknesses of the China Meteorological Administration (CMA) convection‐permitting ensemble prediction system (CPEPS) in representing spread–skill relationships and to identify avenues for improvement, this study applied multidimensional diagnostic metrics—temporal evolution, spatial distribution, spread–skill of perturbations applied at different scales, and point‐to‐point distributions of the spread–skill relationships between the ensemble mean root mean square error (RMSE) and ensemble spread—to operational CMA‐CPEPS data for the period January–June 2025. The results revealed that the CMA‐CPEPS is under‐dispersive for most variables except 500‐hPa geopotential height. Temperature exhibits a poorer spread–skill relationship than that of wind variables, and near‐surface variables show a poorer relationship than that of variables at mid‐ and low‐tropospheric levels. Spread–skill relationships vary regionally, with larger spreads and higher spread–skill ratios over China north of 30° N but smaller spreads and under‐dispersion over China south of 30° N. However, the higher spread–skill ratio over northern China does not yield uniformly larger correlation coefficients between the RMSE and ensemble spread. The point‐to‐point distributions effectively explain the observed six‐month averaged spread–skill relationships. Moreover, contributions of perturbations at different scales on the relationships differ across variables and forecast lead times. Based on these diagnostics, we have identified potential areas for improvements to enhance the spread–skill relationships in the CMA‐CPEPS. The diagnostic methods can be extended to evaluations of other ensemble prediction systems, providing a foundation for continuously improving EPSs.