As a contribution to an Integrated Water Resources Management (IWRM) project in Distrito Federal, Brazil, we address several aspects for a credible downscaling of near-surface air temperature and precipitation using the Statistical DownScaling Model (SDSM4.2). For instance, we apply a detailed screening of predictors, consider the end user needs in the validation procedure, assess the added value of the downscaling model and include several sources of uncertainties until the downscaling step. Results suggest that the interpolation of large-scale predictors to the target site is a reasonable alternative to predictors derived from grid-boxes. The validation metrics, measures (i.e. bias, root-mean-square error, and Pearson's correlation coefficient) and quantile–quantile plots reveal that model tends to underestimate near-surface temperature and precipitation; whereas extreme values are subject of considerable uncertainties. Single-site projections at daily scale are derived from 27 climate models from the Coupled Model Intercomparison Project phase 5 (CMIP5) forced by Representative Concentration Pathways (i.e. RCP2.6, RCP4.5, RCP6.0 and RCP8.5) scenarios. The downscaling model adds substantial value in terms of amplitude of variability when compared to the host coarse-resolution projections. Its performance is higher than a quantile-mapping bias correction technique, particularly in reproducing observed trends. In spite of the elevated level of uncertainties in the magnitude of change, most of the downscaled projections agree on positive changes in near-surface temperature and precipitation for the period of 2036–2055 when compared to the reference period (i.e. 1986–2005). The massive amount of downscaled projections is of limited application in hydrological studies and, therefore, we suggest a summarized group of projections which are representative to the central tendency and spread of the ensemble.