# Towards Long-Range ENSO Prediction with an Explainable Deep Learning Model

arXiv: https://arxiv.org/abs/2503.19502 · HTML: https://mlsanity.com/paper/2503.19502/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 80 references that are in the map.
- First posted on arXiv 25 Mar 2025.

## Builds on (most cited first, 11 of 11)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Visualizing and Understanding Convolutional Networks](https://mlsanity.com/paper/1311.2901/) (2013)
- [MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer](https://mlsanity.com/paper/2110.02178/) (2021)
- [ViTAE: Vision Transformer Advanced by Exploring Intrinsic Inductive Bias](https://mlsanity.com/paper/2106.03348/) (2021)
- [Theoretical Analysis of Inductive Biases in Deep Convolutional Networks](https://mlsanity.com/paper/2305.08404/) (2023)
- [A Random CNN Sees Objects: One Inductive Bias of CNN and Its Applications](https://mlsanity.com/paper/2106.09259/) (2021)
- [NeuralSens: Sensitivity Analysis of Neural Networks](https://mlsanity.com/paper/2002.11423/) (2020)
- [LEFormer: A Hybrid CNN-Transformer Architecture for Accurate Lake Extraction from Remote Sensing Imagery](https://mlsanity.com/paper/2308.04397/) (2023)
- [ResoNet: Robust and Explainable ENSO Forecasts with Hybrid Convolution and Transformer Networks](https://mlsanity.com/paper/2312.10429/) (2023)

## Built on by (most cited first, 4 of 4)

- [Distillation and Interpretability of Ensemble Forecasts of ENSO Phase using Entropic Learning](https://mlsanity.com/paper/2602.16857/) (2026)
- [Leveraging GNN to Enhance MEF Method in Predicting ENSO](https://mlsanity.com/paper/2508.07410/) (2025)
- [Distillation of CNN Ensemble Results for Enhanced Long-Term Prediction of the ENSO Phenomenon](https://mlsanity.com/paper/2509.06227/) (2025)
- [Sensitivity Fidelity Reveals Physical Dependencies Beyond Forecast Accuracy in Deep-Learning Earth-System Models](https://mlsanity.com/paper/2610.15540/) (2026)

## Abstract (arXiv)

El Ni~no-Southern Oscillation (ENSO) is a prominent mode of interannual climate variability with far-reaching global impacts. Its evolution is governed by intricate air-sea interactions, posing significant challenges for long-term prediction. In this study, we introduce CTEFNet, a multivariate deep learning model that synergizes convolutional neural networks and transformers to enhance ENSO forecasting. By integrating multiple oceanic and atmospheric predictors, CTEFNet extends the effective forecast lead time to 20 months while mitigating the impact of the spring predictability barrier, outperforming both dynamical models and state-of-the-art deep learning approaches. Furthermore, CTEFNet offers physically meaningful and statistically significant insights through gradient-based sensitivity analysis, revealing the key precursor signals that govern ENSO dynamics, which align with well-established theories and reveal new insights about inter-basin interactions among the Pacific, Atlantic, and Indian Oceans. The CTEFNet's superior predictive skill and interpretable sensitivity assessments underscore its potential for advancing climate prediction. Our findings highlight the importance of multivariate coupling in ENSO evolution and demonstrate the promise of deep learning in capturing complex climate dynamics with enhanced interpretability.