Hawaiian Islands baseline climate projections for mean annual temperature and precipitation from 1983-2012

Global downscaled projections are now some of the most widely used climate datasets in the world, however, they are rarely examined for representativeness of local climate or the plausibility of their projected changes. Here we show steps to improve the utility of two such global datasets (CHELSA and WorldClim2) to provide credible climate scenarios for regional climate change impact studies. Our approach is based on three steps: 1) Using a standardized baseline period, comparing available global downscaled projections with regional observation-based datasets and regional downscaled datasets (if available); 2) bias correcting projections using observation-based data; and 3) creating ensembles to make use of the differential strengths of global downscaling datasets. We also explored the patterns and magnitude of change for these regional projected climate shifts to determine their plausibility as future climate scenarios using Hawaiʻi as an example region. While our ensemble projections were shown to largely reduce the deviations between model and observation-based current climate, we show projected climate shifts from these commonly used global datasets can fall well outside the range of future scenarios derived from fine-tuned regional downscaling efforts, and hence should be carefully evaluated. This data release includes a baseline (1983-2012) model as well future climate projections for mid- (2040-2059) and late-century (2060-2079) for three regionally-adapted global datasets (CHELSA, WorldClim2, and an ensemble). We considered mean annual temperature (MAT) and mean annual precipitation (MAP) as our primary variables for comparison since they are the most widely used and desired datasets for climate impact studies. These regionally-downscaled future climate projections are available for various individual Global Circulation Models (GCMs) under four representative concentration pathways (RCPs; 2.6, 4.5, 6.0, and 8.5) for each global dataset.

[
  "010:12"
]

[
  "geospatial"
]