Investigating the Role of Artificial Intelligence in Enhancing Climate Change Prediction Accuracy and Supporting Data-Driven Mitigation Strategies

Abstract

This paper explores the role of artificial intelligence (AI) in enhancing the precision of climate change predictions and aiding in data-driven strategies for mitigation. I conducted a systematic literature review focused on secondary data, analyzing peer-reviewed articles, key reports, and trusted industry news from 2018 to 2025. The study highlights various AI techniques utilized in climate modeling, such as downscaling, forecasting extreme events, remote sensing for land use and carbon monitoring, optimizing renewable energy, and supporting decision-making in mitigation policies. The results show that AI—especially through deep learning, graph neural networks, and GeoAI—has the potential to (a) boost forecast accuracy and computational efficiency, (b) facilitate more detailed spatial and temporal downscaling, and (c) introduce innovative monitoring solutions like near real-time mapping of deforestation and wildfire risks. Nevertheless, there are ongoing challenges, including issues of interpretability, the ability to generalize beyond training conditions, the energy demands of large models, and governance and ethical considerations. The study wraps up with suggestions for integrating AI with traditional modeling methods, promoting open data and benchmarking, developing explainable AI techniques, and assessing the lifecycle emissions of AI systems.