General circulation models (GCMs) allow the analysis of potential changes in the climate system under different emissions scenarios. However, their spatial resolution is too coarse to produce useful climate information for impact/adaptation assessments. This is especially relevant for regions with complex orography and coastlines, such as in Chile. Downscaling techniques attempt to reduce the gap between global and regional/local scales; for instance, statistical downscaling methods establish empirical relationships between large-scale predictors and local predictands. Here, statistical downscaling was employed to generate climate change projections of daily maximum/minimum temperatures and precipitation in more than 400 locations in Chile using the analog method, which identifies the most similar or analog day based on similarities of large-scale patterns from a pool of historical records. A cross-validation framework was applied using different sets of potential predictors from the NCEP/NCAR reanalysis following the perfect prognosis approach. The best-performing set was used to downscale six different CMIP5 GCMs (forced by three representative concentration pathways, RCPs). As a result, minimum and maximum temperatures are projected to increase in the entire Chilean territory throughout all seasons. Specifically, the minimum (maximum) temperature is projected to increase by more than 2 degrees C (6 degrees C) under the RCP8.5 scenario in the austral winter by the end of the twenty-first century. Precipitation changes exhibit a larger spatial variability. By the end of the twenty-first century, a winter precipitation decrease exceeding 40% is projected under RCP8.5 in the central-southern zone, while an increase of over 60% is projected in the northern Andes.