Global dataset of downscaled future projections developed by applying a statistical method for climate model downscaling and bias correction. We applied the delta method, which comprises the sum of interpolated anomalies of each GCM to the WorldClim 1-km spatial resolution dataset. The GCMs were the 35 Coupled Model Intercomparison Project Phase 5 (CMIP5) models, for four representative concentrations pathways (RCPs). For each of these, we used the 30-year future periods named as 2030s, 2050s, 2070s and 2080s with three climate variables (mean monthly maximum and minimum temperatures and monthly rainfall). From these, we also derive a set of bioclimatic indices. We divided the global surface in 18 geographical tiles. The primary downscaling resolution is 30 arc-s (~1 km at the Equator) but we aggregate the data to other three resolutions using nearest neighbor interpolation, including: 2.5 arc-m (~5 km), 5 arc-m (~10 km), and 10 arc-m (20 km).
The dataset name structure for this dataset group: "CMIP5 Downscaled - High Resolution Data Files of Tile <LAT><LON>"
where <LAT> is one letter of A (lat>+30°), B (-30°<lat<+30°), C (-60°<lat<-30°) and
and <LON> is one digit of 1,2,3,4,5,6 for the longitudes -180° to -120°, -120° to -60°, and so on.
The file name structure for these datasets: "cmip5dc_tile_<LAT><LON>_<res>_<rcp>_<YYYY>s_asc.zip" where <LAT>,<LON> is as above;
<res> is resolution=30",2.5',5',10'; <rcp> is one of four Representative Concentration Pathways;
and <YYYY> is the time step.