The impact of land use on ecosystems has reached critical levels, jeopardizing biosphere integrity. A key indicator that quantifies, monitors, and analyses such impacts is the Human Appropriation of Net Primary Production (HANPP). Assessing HANPP requires integrating data from sources such as remote sensing and census statistics, as well as modelled data like potential Net Primary Production (NPP), which reflects NPP without land use. Although the availability of global land cover data at high spatial detail from remote sensing has improved, with resolutions reaching 30 arcseconds (about 1 km) and higher, global NPP results from Dynamic Global Vegetation Models (DGVMs) are still unavailable at this resolution. This spatial mismatch causes uncertainties, as simple interpolation methods fail to capture fine-scaled productivity patterns. We here present a parsimonious method to downscale NPP, using the Miami NPP model with temperature and precipitation data as readily available auxiliary information at high spatial resolution. Our method uses a moving window approach with Gaussian convolution to minimize downscaling artefacts. We demonstrate this Smooth Auxiliary Data (SAD) downscaling approach by downscaling potential NPP results from the LPJ-GUESS-DGVM model for the year 2010 from 30arcmin to 30arcsec resolution. This approach, requiring low computational cost, generates fine-scaled productivity patterns and aligns with alternative models for smaller geographic units, offering a solution until high-resolution DGVM results become feasible.