From global climate data to actionable local risk information: lessons from multi-sectoral adaptation activities in Finland

Effective climate change adaptation requires climate information that is not only scientifically robust but also aligned with the diverse needs of end users operating at regional, national, and local scales. These needs vary widely across sectors such as public authorities, municipalities, emergency services, critical infrastructure operators, and insurance providers, creating challenges related to spatial and temporal resolution, uncertainty communication, data formats, and interpretability. In this contribution, we synthesise insights from several ongoing projects to examine how different types of climate information can be tailored to support efficient climate risk assessment and adaptation decision-making.

ILMOS Uusimaa project focuses on municipal-scale adaptation, producing high-resolution climate projections, economic impact assessments of adaptation measures, and storyline-based Climate Digital Twin simulations of extreme events to support practical decision-making. These results demonstrate the importance of kilometre-scale data, impact-oriented indicators, and clear narratives when engaging local actors.

Sector-specific requirements are further illustrated by projects addressing critical infrastructure and financial risk. MAWECLI and WIND‑IMPACT investigate single and compound weather hazards relevant for nuclear safety and electricity networks, respectively, combining physical and statistical modelling, extreme value analysis, and advanced uncertainty quantification. These projects emphasize the need for rare-event statistics, physically consistent simulations, and explicit treatment of compound and cascading risks. Meanwhile, PIISA focuses on co-developing climate-resilient insurance solutions, where probabilistic risk information, loss data, and comparability across regions and hazards are essential.

Finally, the CLAIMS and Climate Digital Twin Storyline analyses of extremes showcase how event-based attribution and counterfactual simulations can make climate change impacts tangible by quantifying how specific events differ between past, present, and warming scenarios. Across all projects, common challenges emerge in balancing resolution and computational cost, integrating uncertainties into decision processes, and translating complex climate data into actionable knowledge. Our synthesis highlights pathways for bridging global climate information with end-user-relevant, locally actionable climate risk assessments.