Artificial Intelligence, Extreme Climate Events, and Disaster Risk Reduction: A Scientific Review
Abstract: Extreme climate events- heatwaves, floods, droughts, wildfires, and intensified tropical cyclones- are increasing in frequency and severity as anthropogenic climate change reshapes the statistical distribution of weather. Traditional numerical weather and climate prediction, built on physics-based equations solved on supercomputers, remains the backbone of forecasting but faces limits in computational cost, resolution, and speed. Over the past five years, artificial intelligence (AI), particularly deep learning, has emerged as a complementary and in some respects transformative tool across the full disaster management cycle: prediction, early warning, real-time monitoring, response coordination, and post-event damage assessment. This article reviews the scientific basis of AI-based weather and climate modeling, surveys applications across major hazard types, examines documented strengths and limitations (including the "blurring" problem in deterministic AI forecasts and the loss of physical interpretability), and discusses governance and equity challenges that will determine whether these tools reduce or exacerbate global disaster risk.
Keywords: Artificial intelligence, Climate change, Weather forecasting, Disaster management, Early warning systems
How to Cite?: Abhishek Mani, "Artificial Intelligence, Extreme Climate Events, and Disaster Risk Reduction: A Scientific Review", Volume 15 Issue 7, July 2026, International Journal of Science and Research (IJSR), Pages: 580-583, https://www.ijsr.net/getabstract.php?paperid=SR26706234208, DOI: https://dx.doi.org/10.21275/SR26706234208