AI-Enhanced Climate Modeling for American Extreme Weather Prediction: Advanced Machine Learning to Unlock Next-Generation Forecasting Powers

Authors: Ishmael Jesse Narh Adikorley, Gabriel Opeyemi Oladipupo

DOI: https://doi.org/10.37082/IJIRMPS.v14.i2.232989

Short DOI: https://doi.org/hbr54b

Country: United States

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Abstract: The intensification and frequency of extreme weather events in the United States present challenges for the traditional numerical weather prediction (NWP) models. New breakthroughs in artificial intelligence (AI) and machine learning (ML) have opened unprecedented opportunities for improving the performance of climate models, particularly the forecast of extreme weather events. In this narrative review, we summarize the latest advances in AI-augmented climate modeling, focusing on the impact of the integration of machine learning on the skill of extreme weather prediction at the U.S. facets of emerging trends, methodological progress and operational implications. We surveyed AI integration with traditional climate models and identified themes from peer-reviewed literature in 2020 to present, including recent advances in neural weather models, hybrid physics-ML approaches, and extreme event detection algorithms. Revolutionary AI models e.g. GraphCast, FourCastNet, and FengWu outperform traditional NWP systems on medium-range forecasting. Deep learning-based methods are particularly promising for extreme event prediction, having achieved record-breaking performance in heatwave, hurricane, and flood detection and prediction using deep convolutional neural networks and transformer-based architectures. Yet, difficulties still exist in the interpretability of models, quantification of uncertainties, and generalization of new extremes. AI-assisted dynamic modulations are game changers in agrometeorological predictions, with superior predictive power for extreme weather events needed for disaster alert systems, risk mitigation and climate change management plans in the USA.

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Paper Id: 232989

Published On: 2026-03-12

Published In: Volume 14, Issue 2, March-April 2026

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