Five principal components are used to represent the climate variation in an original set of 12 climate variables reflecting precipitation and temperature gradients. The dataset provides coverage for four regions (the Sonoran Desert, Mojave Desert, Colorado Plateau, and Southern Great Basin) and three time periods including current climate (defined as the 1980-2010 normal period) and future climate (2010-2040 and 2040-2070 normal periods) under the RCP4.5 and RCP8.5 emission scenarios. Climate variables were chosen based on their known influence on local adaptation in plants, and include: mean annual temperature, summer maximum temperature, winter minimum temperature, annual temperature range, temperature seasonality (coefficient of variation in monthly average temperatures), mean annual precipitation, winter precipitation, summer precipitation, proportion of summer precipitation, precipitation seasonality (coefficient of variation in monthly precipitation totals), long-term winter precipitation variability, and long-term summer precipitation variability. The conversion to principal components both standardizes and accounts for covariation in climate variables, while emphasizing the most important climate gradients across the landscape. Raster layers representing each principal component form the input to Climate Distance Mapper (https://usgs-werc-shinytools.shinyapps.io/Climate_Distance_Mapper/), an interactive R Shiny application for matching seed sources with restoration sites. Plant populations are commonly adapted to local climate gradients and frequently exhibit a home-site advantage. For this reason, climate information may serve as a proxy for local adaptation in restoration designs. Climate Distance Mapper allows users to rank the suitability of seed sources for restoration sites by displaying multivariate climate distances (incorporating climate principal components) from user-supplied input points to the surrounding landscape. The application provides functions to match seed sources with current or future climate, guide sampling effort for large scale seed collections, and partition the landscape into suitable areas for different seed sources. These data support the following publication: Shryock, D.F., DeFalco, L.A., and T.C. Esque. 2018. Spatial decision-support tools to guide restoration and seed sourcing in the Desert Southwest. Ecosphere 9(10):e02453.