The mountain regions of the Northeastern US are a critical socioeconomic resource for Vermont, New York State, New Hampshire, Maine, and Southern Quebec. While global climate models (GCMs) are important tools for climate change risk assessment at regional scales, even the increased spatial resolution of statistically downscaled GCMs (commonly ~1/8°) is not sufficient for hydrologic, ecologic, and land-use modeling of small watersheds within the mountainous Northeast. To address this limitation, we develop an ensemble of topographically downscaled, high-resolution (30”), daily 2-m maximum air temperature, 2-m minimum air temperature, and precipitation simulations for the mountainous Northeast by applying an additional level of downscaling to intermediately downscaled (1/8°) data using high-resolution topography and station observations. We first derive observed relationships between 2-m air temperature and elevation, and precipitation and elevation. Then, these relationships are combined with spatial interpolation to enhance the resolution of intermediately downscaled GCM simulations. The resulting topographically downscaled dataset is analyzed for its ability to reproduce station observations. We find that topographic downscaling adds value to intermediately downscaled maximum and minimum 2-m air temperature at high elevation stations, as well as moderately improves domain-averaged maximum and minimum 2-m air temperature. Topographic downscaling improves mean precipitation but not daily probability distributions of precipitation. Overall, we show that the utility of topographic downscaling is dependent on the initial bias of the intermediately downscaled product and the magnitude of the elevation adjustment. As the initial bias or elevation adjustment increase, more value is added to the topographically downscaled product.