Recently, Professor Fan Jingfang's team from the School of Systems Science at Beijing Normal University, in collaboration with Institute of Atmospheric Physics at  Chinese Academy of Sciences, Beijing University of Posts and Telecommunications, and other research institutions, published a research paper titled "Enhancing the predictability limits of ENSO with physics-guided Deep Echo State Networks" in npj Climate and Atmospheric Science. The study proposes a novel prediction framework that integrates climate physics mechanisms with artificial intelligence algorithms, providing a new theoretical and technical approach to improve the long-term prediction capabilities of the El Niño-Southern Oscillation (ENSO).

The abstract of this paper is as follows:

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.

Reference: https://www.nature.com/articles/s41612-026-01360-5