AI-Powered Climate Adaptation Strategies

Abstract

Climate adaptation -- adjusting human systems, infrastructure, and ecosystems to reduce vulnerability to observed and anticipated climate change impacts -- requires decision-making under deep uncertainty at multiple spatial and temporal scales simultaneously. Climate impacts are non-linear, geographically heterogeneous, and interact with socioeconomic systems in ways that conventional planning tools cannot fully capture. AI offers three critical capabilities for climate adaptation: high-resolution climate impact downscaling that translates global model projections to actionable local scales, multi-criteria adaptation pathway optimisation under uncertainty, and real-time early warning systems for extreme weather events. This study evaluates eight AI systems across five climate adaptation domains: sea level rise and coastal flood risk, urban heat island mitigation, agricultural drought adaptation, biodiversity corridor planning, and infrastructure resilience assessment. AI systems evaluated include statistical downscaling (BCSD-ML), deep learning climate emulators (ClimaX-Adapt), computer vision for land cover change detection (ChangeFormer-CC), extreme event forecasting (ExtremeCast), multi-objective adaptation optimisation (MAOP), agent-based climate migration modelling (ABCM), ecosystem service modelling (ESM-AI), and an integrated Climate Adaptation Decision Support System (CADSS). Evaluation uses observational data from 2010-2024 across eight European climate regions and comparison against conventional planning baselines. ClimaX-Adapt achieves 18.4% improvement in local temperature extreme prediction. MAOP identifies adaptation portfolios reducing projected flood damage by 42.4% at 2.8x lower cost than conventional approaches. ExtremeCast achieves 84.2% recall for extreme precipitation events at 72-hour lead time. A governance framework for AI-assisted climate adaptation planning is proposed.