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This is a Quantitative Study study.

Machine learning-based predictive analysis of ENSO response to temperature and precipitation in southwestern mountains

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The random forest model predicts temperature with a mean absolute error of 1.67°C, outperforming xgboost.
Precipitation predictions exhibit higher error rates, with generally low accuracy for extreme weather events.
Machine learning methods reveal the significant impact of enso on regional climate patterns and forecasting.
Further integration of local environmental factors is needed to enhance precipitation prediction accuracy.

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Machine learning models evaluate ENSO interactions with temperature and precipitation, suggesting significant implications for climate forecasting.

Yao et al. (2025) studied this question.

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Machine learning models evaluate ENSO interactions with temperature and precipitation, suggesting significant implications for climate forecasting.