Tang et al. (2026) Skillful Probabilistic Prediction of Seasonal Extreme Precipitation Based on China Multi‐Model Ensemble Forecasts

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Identification

• Journal: International Journal of Climatology
• Year: 2026
• Date: 2026-08-26
• Authors: Wenrui Tang, Jie Wu, Hong‐Li Ren, Guo Li, Jianghua Wan, Jingpeng Liu
• DOI: 10.1002/joc.70548

Research Groups

• National Meteorological Information Center (NMIC)
• Institute of Atmospheric Physics (IAP), Chinese Academy of Sciences (CAS)

Short Summary

This study evaluates the China Multi-Model Ensemble (CMME) system's ability to predict summer extreme precipitation anomalies and identifies the factors contributing to its improved performance. The results show that CMME outperforms single-model predictions, particularly in tropical regions.

Objective

• Investigate the skill improvement of multi-model ensemble (MME) predictions over single-model deterministic dynamical systems for seasonal extreme precipitation anomalies

Study Configuration

• Spatial Scale: Global and regional scales, focusing on China and surrounding areas
• Temporal Scale: Summer season (June to August)

Methodology and Data

• Models used: CMME system, consisting of multiple models from different institutions
• Data sources: Reanalysis data (e.g., ERA5), satellite observations, and ground-based measurements

Main Results

• CMME outperforms single-model predictions in probabilistic prediction of summer extreme precipitation anomalies
• Model diversity enhances prediction reliability, particularly in tropical regions
• Increasing ensemble size and probability distribution spread improves predictions for mid-to-high latitude regions (e.g., East Asia)

Contributions

• Provides a robust scientific basis for the objective extraction and utilisation of probabilistic information in seasonal extreme prediction
• Identifies optimal probability threshold for translating probabilistic forecasts into deterministic warnings

Funding

• National Key Research and Development Program of China (2018YFC1503802)
• National Natural Science Foundation of China (41875044)

Citation

@article{Tang2026Skillful,
  author = {Tang, Wenrui and Wu, Jie and Ren, Hong‐Li and Li, Guo and Wan, Jianghua and Liu, Jingpeng},
  title = {Skillful Probabilistic Prediction of Seasonal Extreme Precipitation Based on China Multi‐Model Ensemble Forecasts},
  journal = {International Journal of Climatology},
  year = {2026},
  doi = {10.1002/joc.70548},
  url = {https://doi.org/10.1002/joc.70548}
}