Climate change poses one of the most critical challenges of our time, intensifying the frequency and severity of extreme weather events, sea-level rise, and environmental degradation. Traditional climate modeling systems, primarily physics-based, are powerful but limited by high computational demands, long runtimes, and coarse resolutions. In recent years, artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), has emerged as a transformative tool in climate modeling and forecasting. By leveraging vast amounts of observational, satellite, and reanalysis data, AI models can identify complex nonlinear patterns, improve forecast accuracy, and dramatically reduce computational costs. AI-driven systems like FourCastNet, Atmo AI, and QuickClim deliver faster, more granular forecasts, enhancing early warning systems for disasters such as floods, cyclones, and heatwaves. This paper explores the architecture, real-world applications, and resilience-enhancing capabilities of AI-driven climate models, emphasizing their role in supporting policymakers, urban planners, agricultural managers, and emergency responders. While promising, the use of AI in climate science also raises challenges around data quality, explainability, ethical concerns, and environmental costs related to model training. Addressing these challenges is key to ensuring AI's responsible and equitable deployment. This review synthesizes cutting-edge research, performance metrics, and real-time applications, providing insights into how predictive analytics can strengthen global environmental resilience.
M. Chandra Naik (Thu,) studied this question.
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