Gridded temperature data sets are typically produced at spatial resolutions that cannot fully resolve fine-scale variation in surface air temperature in regions of complex topography. These data limitations have become increasingly important as scientists and managers attempt to understand and plan for potential climate change impacts. Here, we describe the development of a high-resolution (250 m) daily historical (1979–2012) temperature data set for the US Northern Rocky Mountains using observations from both long-term weather stations and a dense network of low-cost temperature sensors. Empirically based models for daily minimum and maximum temperature incorporate lapse rates from regional reanalysis data, modelled daily solar insolation and soil moisture, along with time invariant canopy cover and topographic factors. Daily model predictions demonstrate excellent agreement with independent observations, with mean absolute errors of <1.4 °C for both minimum and maximum temperature. Topographically resolved temperature data may prove useful in a range of applications related to hydrology, fire regimes and fire behaviour, and habitat suitability modelling. The form of the models may provide a means for downscaling future temperature scenarios that account for potential fine-scale topographically mediated changes in near-surface temperature.