High-resolution topoclimate data is essential for predicting species distributions in mountains: insights from CHclim25

Abstract Species distribution models (SDMs) are widely used to anticipate biodiversity responses to climate change, yet most rely on coarse climatic datasets that fail to capture the fine-scale thermal heterogeneity characteristic of mountain environments. This mismatch is particularly problematic for alpine plants, which abiotic environment is characterized by strong topographic gradients that generate substantial climatic heterogeneity over short distances. Here we argue that improving the spatial and temporal representation of climate datasets based on standard 2 m air temperature measurements is essential for ecologically realistic SDMs in mountains. Using CHclim25, a newly developed 25 m resolution daily topoclimate dataset for Switzerland, we show that fine-scale climatic predictors better represent the spatial and temporal variability in air temperature across elevation gradients. Comparisons with WorldClim and CHELSA demonstrate that CHclim25 more accurately reproduces air temperature patterns, especially in topographically complex terrain. While CHclim25 does not explicitly simulate organism-level microclimates, it provides a substantially improved representation of near-surface temperature conditions at spatial grains closer to ecological observations. When integrated with high-resolution environmental datasets such as SWECO25 and national-scale SDM initiatives like SDMapCH, CHclim25 enables the identification of fine-scale gradient, the mapping of fine-scale habitat structure, and more reliable projections of species’ climatic niches. The growing availability of very high-resolution elevation models further strengthens the potential for similar downscaling approaches in other mountain regions. Together, these developments highlight the need for SDMs that incorporate fine-scale climatic variation and call for broader adoption of high-resolution climatic datasets to improve biodiversity forecasting in alpine landscapes.