To successfully protect native biodiversity from the effects of biological invasions, local conservation priorities must be established. For this purpose, fine-grained species distribution data is required but often unavailable. We present a new approach to obtain fine-grained predictions of invasion through the development of downscaled invasion maps based on coarse-grained distribution data. The framework is illustrated for the alien invader Acacia dealbata in the Northwest of Portugal. The analytical design was divided in five steps: (1) three individual coarse-grained models were calibrated and their spatial predictions were downscaled into fine-grained models using three different downscaling techniques; (2) a Downscaling Consensus Map was built by spatially combining the predictions from those three models; (3) using coarse-grained (1 km2) or fine-grained (0.04 km2) datasets, two different models were fitted and spatially projected; (4) for each spatial resolution, Conservation Value maps were produced, based on the spatial combination of the protection networks represented in the region; and (5) the spatial conflicts between the predicted distribution of the invader and Conservation Value maps were calculated and compared for the several invasion maps. The downscaled models showed high predictive performance (AUC > 0.9). The spatial projections of the different models revealed a general similarity among projections from all modelling techniques, for both the patterns of invasion and the conflicts with conservation areas. The possibility of obtaining detailed and reliable predictions based on coarse-grained distribution data could avoid costly fieldwork to collect fine-grained distribution data while effectively supporting the management of invasions at the appropriate scales.