As climate change intensifies extreme weather events from heatwaves to floods, global climate models alone cannot address the urgent need for localized adaptation strategies that protect communities where policy and planning decisions are actually implemented. A new perspective published in Frontiers of Environmental Science & Engineering highlights the critical gap between broad global projections and the granular reality of local impacts, where topography, urbanization patterns, and socioeconomic conditions create unique vulnerabilities that demand tailored modeling approaches.
The study, available at https://doi.org/10.1007/s11783-025-2091-7, argues that local-scale models operating at city, regional, or national levels are indispensable for designing effective adaptation measures. These high-resolution tools integrate environmental data, social factors, and real-time urban information to simulate fine-grained climate variations, identify vulnerable areas, and evaluate adaptation scenarios before implementation. Without such detailed modeling, adaptation measures risk being overly generalized or ineffective against the specific threats communities face.
Current challenges in local-scale model development include limited data availability, lack of multi-scale integration, and the complexity of coupling climate dynamics with socioeconomic systems. To overcome these barriers, researchers recommend advancing data integration through satellite remote sensing, machine learning, and collaborative platforms like the World Urban Database. Emerging "One Atmosphere" and "Seamless Earth System" modeling approaches that link global and local processes show particular promise for improved consistency and feedback mechanisms.
Artificial intelligence and physics-informed machine learning are expected to revolutionize model calibration, making these tools more efficient and accessible to developing countries that face the greatest climate risks but often have the fewest resources. By combining environmental science with digital technologies, local-scale modeling can become a cornerstone of evidence-based adaptation planning, early warning systems, and long-term climate-resilient urban design.
"Local-scale modeling marks the next frontier of climate adaptation," said Prof. Alexander Baklanov, co-author from the University of Copenhagen. "Global models give us the big picture, but communities live the consequences locally—where geography, infrastructure, and human behavior intersect. We urgently need multi-scale, interoperable models that can translate global climate projections into actionable, context-specific insights."
The accessibility of these modeling frameworks through open-source platforms and AI-enhanced tools enables adoption even in resource-limited regions, potentially transforming how communities worldwide prepare for climate impacts. The authors urge governments, researchers, and international organizations to prioritize co-developing such models as part of national adaptation plans, emphasizing that strengthening local modeling capacity today will be crucial for achieving sustainable, resilient societies in the coming decades.
