When and where higher-resolution climate data improve impact model performance

Climate impact assessments increasingly rely on high-resolution climate and forcing datasets, under the premise that finer detail enhances both the accuracy and the policy relevance of projections. Systematic evaluations of when and where higher resolution data improve model outcomes remain limited, and it is still unclear whether increasing spatial resolution consistently enhances climate impact model performance across application areas, regions, and forcing variables. Here we show that improvements in climate input accuracy and impact model performance are most pronounced when moving from coarse (60 km) to intermediate (10 km) resolution, while further refinement to 3 km and 1 km provides more modest and inconsistent benefits. Using the cross-sectoral model simulations from the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP), we demonstrate that higher resolution substantially improves model skill in temperature-sensitive impact models and topographically complex regions, whereas precipitation-driven and low-relief systems show less consistency to increase performance with resolution. For temperature, both climate inputs and model outputs improved most strongly at the 60 km → 10 km transition, with diminishing gains at finer scales. A similar result emerged for precipitation, although some models even exhibited reduced performance when resolution increased beyond 10 km. These results highlight that optimal resolution depends on sectoral and regional context, and point to the need for improving model process representation and downscaling techniques so that added spatial detail can translate into meaningful performance gains. For data providers, this implies prioritizing investments in resolutions that maximize improvements where they matter most, while for modeling groups and users, it underscores the need for explicit benchmarking of resolution choices. More broadly, this work advances the design of consistent, efficient, and policy-relevant multi-sectoral climate impact assessments by clarifying when high-resolution data meaningfully enhance outcomes.