ENSO‐CausalNet: Integrating Causal Inference Into Deep Learning for Robust ENSO Prediction

Abstract Climate mode interactions are inherently causal. While deep learning excels at climate prediction, particularly for El Niño‐Southern Oscillation (ENSO), its interpretability remains limited to validating known correlations. Here we propose a paradigm integrating causal inference into data‐driven modeling to enable predictions based on genuine causal relationships. Following this paradigm, we develop ENSO‐CausalNet, achieving skillful ENSO prediction of the Niño 3.4 index up to 22 months ahead. Results reveal that dominant physical processes affecting ENSO vary with lead time, elucidating distinct causal pathways through which Bjerknes feedback and extratropical Pacific, Atlantic, and Indian Ocean air‐sea interactions drive ENSO variability. When input dimensionality increases, the model may learn incomplete causal relationships, resulting in degraded prediction skill. These findings demonstrate that forecast capability depends critically on comprehensive causal understanding, confirming the model's physical validity. This paradigm provides robust ENSO predictions extensible to other climate systems, enabling mechanistic analysis and scientific discovery.