As climate change intensifies extreme weather events, global climate models alone cannot provide the detailed information needed for effective local adaptation planning. A new perspective published in Frontiers of Environmental Science & Engineering highlights the urgent need for high-resolution, local-scale modeling tools that integrate environmental, social, and economic dynamics to support climate adaptation and sustainable development. The study, available at https://doi.org/10.1007/s11783-025-2091-7, argues that while global models have advanced understanding of large-scale processes, they lack the resolution to address local impacts where policy and planning decisions are actually implemented. Regional variations in topography, urbanization, and socioeconomic conditions demand more granular data and simulation capabilities that can bridge the gap between global projections and local realities.
Local-scale models operating at city, regional, or national levels can simulate fine-grained variations in climate conditions by incorporating topography, land use, demographics, and infrastructure data. This enables identification of vulnerable areas and evaluation of adaptation scenarios specific to local contexts. The authors note that without such detail, adaptation measures risk being overly generalized or ineffective against the specific threats communities face. The paper identifies several challenges in developing these models, including 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 data platforms such as 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. 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. Prof. Alexander Baklanov, co-author from the University of Copenhagen, stated that "local-scale modeling marks the next frontier of climate adaptation. 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. The authors urge governments, researchers, and international organizations to prioritize the co-development of 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.
