Mountain glacier evolution since the last interglacial remains poorly constrained, with limited spatial and temporal coverage. Conventional modelling approaches typically target major climatic events, operate at coarse spatial resolution over limited spatial domains, or employ simplified representations of ice dynamics. Here, we address these limitations by applying the Instructed Glacier Model (IGM), to reconstruct, for the first time, mountain glacier evolution since ∼130 ka at 500 m resolution across nine mountain ranges in North America, South America, Eurasia, and Africa. We perform 707 parameter-calibration simulations by varying paleoclimate and ice-dynamic parameters, and validate model performance by assessing glacier extent and ice thickness. The model outputs are evaluated using a spatial frequency map approach, which identifies a set of acceptable model results rather than a single best-fit simulation. As a result, we identify areas of robust agreement and those sensitive to parameter choices, providing a systematic way to visualise spatial uncertainty and glacier–climate-topography interactions. Together, our framework is a scalable foundation for next-generation, uncertainty-aware reconstructions turning glacier modelling at orbital-timescales into a reproducible, expandable workflow that can be deployed across mountain ranges worldwide. All data are publicly available at <a href="https://archive.sigma2.no/dataset/evolution-of-mountain-glaciers-since-the-last-interglacial" target="_blank" rel="noopener">https://archive.sigma2.no/dataset/evolution-of-mountain-glaciers-since-the-last-interglacial</a> (Barndon et al., 2026b) and the animations at <a href="https://av.tib.eu/series/2022" target="_blank" rel="noopener">https://av.tib.eu/series/2022</a> (Barndon et al., 2026a).