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<p>Accurate prediction of global seasonal precipitation anomalies (GSPA) is essential for mitigating flood and drought risks worldwide. However, current climate models exhibit limited skill in predicting GSPA, particularly in extratropical regions. While climate modes are acknowledged as major sources of predictability for GSPA, their specific contributions remain unclear. In this study, we first identify leading climate modes from tropical outgoing longwave radiation anomalies and extratropical 500&#xa0;hPa geopotential height anomalies using EOF decomposition. We then develop a framework to independently reconstruct GSPA based on these modes, applying a 30‑year sliding temporal window. By calculating the temporal correlation coefficient between the reconstructed and observed precipitation anomalies at each grid point, we quantitatively assess the potential predictability of GSPA using an optimal combination of climate modes for each season. Our results show that high potential predictability exists not only in the tropics where the leading mode associated with ENSO dominates, but also across various extratropical regions, especially in winter hemispheres, where the leading mode such as NAO or AAO plays a major role. The potential predictability derived here represents an upper limit achievable if these climate modes are perfectly predicted. Nevertheless, state‑of‑the‑art coupled general circulation models (CGCMs) show clear deficiencies in predicting extratropical seasonal precipitation, largely due to their limited skill in predicting extratropical climate modes. This highlights that improving CGCMs’ prediction of extratropical climate modes is crucial for advancing seasonal precipitation prediction in extratropical regions.</p>

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Potential predictability of global seasonal precipitation anomalies determined by climate modes

  • 期刊论文

  • 发表日期：

    2026年3月16日

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  • Yu Wang,
  • Xiu-Qun Yang,
  • Dejian Yang,
  • Lingfeng Tao,
  • Jiabei Fang,
  • Xuguang Sun

摘要

Accurate prediction of global seasonal precipitation anomalies (GSPA) is essential for mitigating flood and drought risks worldwide. However, current climate models exhibit limited skill in predicting GSPA, particularly in extratropical regions. While climate modes are acknowledged as major sources of predictability for GSPA, their specific contributions remain unclear. In this study, we first identify leading climate modes from tropical outgoing longwave radiation anomalies and extratropical 500 hPa geopotential height anomalies using EOF decomposition. We then develop a framework to independently reconstruct GSPA based on these modes, applying a 30‑year sliding temporal window. By calculating the temporal correlation coefficient between the reconstructed and observed precipitation anomalies at each grid point, we quantitatively assess the potential predictability of GSPA using an optimal combination of climate modes for each season. Our results show that high potential predictability exists not only in the tropics where the leading mode associated with ENSO dominates, but also across various extratropical regions, especially in winter hemispheres, where the leading mode such as NAO or AAO plays a major role. The potential predictability derived here represents an upper limit achievable if these climate modes are perfectly predicted. Nevertheless, state‑of‑the‑art coupled general circulation models (CGCMs) show clear deficiencies in predicting extratropical seasonal precipitation, largely due to their limited skill in predicting extratropical climate modes. This highlights that improving CGCMs’ prediction of extratropical climate modes is crucial for advancing seasonal precipitation prediction in extratropical regions.