The El Ni?o-Southern Oscillation (ENSO) is the most significant interannual climate variability phenomenon, exerting profound influences on global weather patterns and climate anomalies. The associated natural disasters pose severe threats to human lives and property. Traditional ENSO prediction methods primarily include dynamical and statistical approaches. Due to the long-term accumulation of climate data and a well-established theoretical foundation of ENSO dynamics, these methods have been extensively developed. Studies have shown that traditional methods perform well within the first 6 months of forecasting, achieving a correlation coefficient skill (Corr) of up to 0.85. However, prediction accuracy declines over time, with most models struggling to maintain a Corr above 0.5 beyond 12 months. This limitation is largely attributed to the inherent nonlinearity and uncertainty of ENSO events, which challenge the ability of traditional models to improve prediction accuracy and extend forecast lead times. Additionally, computational error accumulation, empirical limitations, and uncertainties in parameter optimization restrict the effectiveness of dynamical models for key long-term ENSO prediction. Likewise, due to the highly nonlinear nature of ENSO onset and evolution, statistical models struggle to capture the complex intrinsic features of ENSO from large datasets, thereby limiting prediction accuracy.
In recent years, deep learning techniques have garnered increasing attention in ENSO forecasting due to their ability to efficiently process complex spatiotemporal data and adaptively learn feature representations. Researchers have explored deep learning approaches for ENSO prediction, achieving promising results. This review provides a comprehensive discussion of ENSO prediction, beginning with an overview of ENSO-related knowledge, including key datasets for ENSO classification and forecasting. It then examines traditional ENSO prediction methods, covering both dynamical and statistical approaches. The review further explores the application of deep learning models in ENSO forecasting, including methods based on convolutional neural networks (CNNs), recurrent neural networks (RNNs), graph neural networks (GNNs), and Transformer models. The advantages, limitations, and development trends of each type approach are summarized.
Despite the promising advancements in deep learning for ENSO prediction, several key challenges remain: 1) The “black-box” nature of deep learning models limits the physical interpretability of predictions. Although efforts have been made to integrate physical knowledge with deep learning, research on model interpretability remains incomplete. 2) The limited time span of ENSO observational data and the rarity of extreme ENSO events result in constrained training samples. Additionally, discrepancies between simulated and observed data pose challenges, necessitating further exploration of multivariate information to enhance model performance. 3) The ongoing rapid changes in global climate may alter ENSO characteristics, making deep learning models trained on historical data susceptible to reduced reliability. Incorporating climate change impacts into deep learning models is essential for improving forecast robustness.