There has been significant scientific discord over what the best resolution for forecasting the impacts of climate change on agriculture and biodiversity is. Several researchers (particularly climatic researchers) state that original GCM (General Circulation Model) resolution should be kept in order to manage, understand and not bias or alter uncertainties produced by GCMs themselves; however, a coarse resolution of 100 or 200km (or even more) is simply not practical for assessing agricultural landscapes, particularly in the tropics, where orographic and climatic conditions vary significantly across relatively small distances. Moreover, changes in topography and climate variables are not the only factors accounting for variability in agriculture; soils and socioeconomic drivers, also often differ over small distances, influencing agro-ecosystems, increasing uncertainties, and making forecasting and assessment models more inaccurate and complicated to calibrate. Here we present a downscaling method as well as a global database on climate change data that can be used for crop modeling, niche modeling, and more generally, for assessing impacts of climate change on agriculture at fine scales, using any approach that might require monthly maximum, minimum, mean temperatures and monthly total precipitation (from which a set of bioclimatic indices were also derived). This database (with a total of 441 different scenarios –the sum of 24, 20 and 19 GCMs, times 7 time-slices) complements other existing databases that also use downscaling but are only available either for a limited set of GCMs, time-slices, regions, or for variables or at coarser resolution. As such, we provide the most current and comprehensive set of climate change ready-to-use datasets, available online at https://ccafs-climate.org.