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

Abstract

Climate impact assessments increasingly rely on high-resolution climate and forcing datasets, under the premise that finer detail enhances both the accuracy and policy relevance of projections. Yet systematic evaluations of when and where higher resolution actually improves impact model outcomes remain limited, and it is unclear whether increasing spatial resolution consistently enhances performance across sectors, regions, and forcing variables. Here we show that gains in climate input accuracy and impact model performance are largest when moving from coarse (60 km) to intermediate (10 km) resolution, while further refinement to 3 km and 1 km yields more modest and inconsistent benefits. Using cross-sectoral simulations from the Inter-Sectoral Impact Model Intercomparison Project, we find that higher resolution substantially improves model skill in temperature-sensitive impact models and topographically complex regions, whereas precipitation-driven and low-relief systems show weaker and less systematic improvements. For temperature, both climate inputs and model outputs improve most strongly at the 60 km to 10 km transition, with diminishing gains at finer scales; for precipitation, some models even exhibit reduced performance 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 translates into meaningful skill gains. For data providers, this implies prioritizing resolutions that maximize improvements where they matter most, while for modelling groups and users it underscores the need for explicit benchmarking of resolution choices in climate impact assessments.