Advanced Deep Learning Combined with Contribution Analysis for Interpretable ENSO Forecasting

Accurate prediction of the El Niño–Southern Oscillation (ENSO) is crucial for understanding and anticipating global climate variability. Although deep learning (DL)-based models have recently improved ENSO forecasting skill, achieving strong predictive performance while maintaining model interpretability remains a major challenge. Existing approaches may suffer from instability or have difficulty distinguishing contributions across multiple variables and time steps. To address this issue, this study presents an interpretable ENSO forecasting framework that combines a ConvNeXt-based deep learning model, ENSO-ConvNeXt, with an improved gradient-based contribution analysis method whose calculation strategy is adjusted according to different ENSO phases. The simplified ConvNeXt architecture facilitates the integration of interpretability methods while retaining strong predictive capability. ENSO-ConvNeXt achieves competitive forecasting skill with an effective lead time exceeding 20 months, accurately capturing the Niño3.4 index evolution during the peak season and the temporal evolution of ENSO events. The case studies of representative ENSO events demonstrate that the major contribution regions identified by the model are broadly consistent with established ENSO variability patterns across major ocean basins. These results highlight the potential of our framework to advance ENSO prediction while providing statistically grounded and physically interpretable insights.