Multi-component Weather Prediction using Self-Supervised Multi-Fidelity Ensemble Learning with Model Explainability

Accurate weather prediction remains a persistent challenge due to the multi-component nature of atmospheric systems and the limitations of traditional forecasting models. This study presents an Artificial Intelligence (AI)–driven weather forecasting framework that integrates Machine Learning (ML), Deep Learning (DL), self-supervised learning, ensemble learning, and explainable AI techniques. Particularly, we propose a Self-Supervised Multi-Fidelity Ensemble Learning (SSMFEL) model, which integrates low-, mid-, and high-fidelity long short-term memory architectures with regularization, dropout, and a decoder to extract meaningful patterns from unlabeled data. The proposed model achieved over 95% R-squared ( R 2 ) metric, coefficient of determination, for humidity and temperature prediction and more than 52% R 2 for wind speed and precipitation, significantly outperforming existing ML, DL, and transformer-based models. SSMFEL also demonstrated the 0.00018 Mean Squared Error (MSE) and 0.03489 root MSE across all components. Results from test statistic tests , sensitivity analysis, and fairness evaluation further confirmed the statistical superiority of the proposed approach. Model explainability analysis revealed that specific humidity is the most influential feature, while temperature and relative humidity contribute contextually. Theoretical analysis based on bias–variance reduction and generalization theory further supports the improved forecasting performance of the proposed method.