Enhancing the predictability limits of ENSO with physics-guided deep echo state networks
Zejing Zhang, Jun Meng, Zhongpu Qiu, Wansuo Duan, Jian Hui Gao, Zixiang Yan, Jinghua Xiao, Xiaosong Chen, Wenju Cai, Jürgen Kurths, Shlomo Havlin, Jingfang Fan · npj Climate and Atmospheric Science · 2026
Abstract The El Niño-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 Niño 3.4 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.