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arXiv · 2601.12251

Long-term prediction of ENSO with physics-guided Deep Echo State Networks

Zejing Zhang·Jun Meng·Zhongpu Qiu·Wansuo Duan·Jian Gao·Zixiang Yan·Jinghua Xiao·Xiaosong Chen·Wenju Cai·Jürgen Kurths·Shlomo Havlin·Jingfang Fan
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Abstract

The El Ni\~{n}o-Southern Oscillation (ENSO) is a dominant mode of interannual climate variability, yet the mechanisms limiting its long-lead predictability remain unclear. Here we develop a physics-guided Deep Echo State Network (DESN) that operates on physically interpretable climate modes selected from the extended recharge oscillator (XRO) framework. DESN achieves skillful Ni\~{n}o3.4 predictions up to 16-20 months ahead with minimal computational cost. Mechanistic experiments show that extended predictability arises from nonlinear coupling between warm water volume and inter-basin climate modes. Error-growth analysis further indicates a finite ENSO predictability horizon of approximately 30 months. These results demonstrate that physics-guided reservoir computing provides an efficient and interpretable framework for diagnosing and predicting ENSO at long lead times.

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Zejing Zhang, Jun Meng, Zhongpu Qiu, Wansuo Duan, Jian Gao, Zixiang Yan, Jinghua Xiao, Xiaosong Chen, Wenju Cai, Jürgen Kurths, Shlomo Havlin, Jingfang Fan. 2026-01-18. Long-term prediction of ENSO with physics-guided Deep Echo State Networks. https://arxiv.org/abs/2601.12251

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