We integrated recent climate model projections developed for the State of Hawai’i with current climatological datasets to generate updated regionally defined bioclimatic variables. We derived updated bioclimatic variables from new projections of baseline and future monthly minimum, mean, and maximum temperature (Tmin, Tmean, Tmax) and mean precipitation (Pmean) data at 250 m resolution. We used observation-based data for the baseline bioclimatic variables from the Rainfall Atlas of Hawai’i. We used the most up-to-date dynamically downscaled future projections based on the Weather Research and Forecasting (WRF) model from the International Pacific Research Center (IPRC) and the National Center for Atmospheric Research (NCAR). We summarized the monthly data from these two projections into a suite of 19 bioclimatic variables that provide detailed information about annual and seasonal mean climatic conditions specifically for the Hawaiian Islands. These bioclimatic variables are available state-wide for three climate scenarios: baseline climate (1990-2009) and future climate (2080-2099) under RCP 4.5 (IPRC projections only) and RCP 8.5 (both IPRC and NCAR projections). As Hawai’i is characterized by two 6-month seasons, we also provide mean seasonal variables for all scenarios based on the dry (May-October) and wet (November-April) seasonality of Hawaiian climate.

We integrated recent climate model projections developed for the State of Hawai’i with current climatological datasets to generate updated regionally defined bioclimatic variables. We derived updated bioclimatic variables from new projections of baseline and future monthly minimum, mean, and maximum temperature (Tmin, Tmean, Tmax) and mean precipitation (Pmean) data at 250 m resolution. We used observation-based data for the baseline bioclimatic variables from the Rainfall Atlas of Hawai’i. We used the most up-to-date dynamically downscaled future projections based on the Weather Research and Forecasting (WRF) model from the International Pacific Research Center (IPRC) and the National Center for Atmospheric Research (NCAR). We summarized the monthly data from these two projections into a suite of 19 bioclimatic variables that provide detailed information about annual and seasonal mean climatic conditions specifically for the Hawaiian Islands. These bioclimatic variables are available state-wide for three climate scenarios: baseline climate (1990-2009) and future climate (2080-2099) under RCP 4.5 (IPRC projections only) and RCP 8.5 (both IPRC and NCAR projections). Aside from these typical bioclimatic variables, we also calculated annual and mean seasonal variables for all scenarios based on the dry (May-October) and wet (November-April) seasonality of Hawaiian climate. As Hawai’i is characterized by two 6-month seasons, we also provide mean seasonal variables for all scenarios based on the dry (May-October) and wet (November-April) seasonality of Hawaiian climate.

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.

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 apply steps to improve the utility of two such global datasets (CHELSA and WorldClim2) to provide credible climate scenarios for climate change impact studies in Hawaii. 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. This data release includes a baseline (1983-2012) model as well as future climate projections for mid- (2040-2059) and late-century (2060-2079) for three regionally-adapted global datasets (CHELSA, WorldClim2, and a combined 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.

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.

One of the determinants of runoff is the occurrence of excess rainfall events where rainfall rates exceed the infiltration capacity of soils. To help understand runoff risks, we calculated the probability of excess rainfall events across the Hawaiian landscape by comparing the probability distributions of projected rainfall frequency and land cover-specific infiltration capacity. We characterized soil infiltration capacity based on different land cover types (bare soil, grasses, and woody vegetation) and compared them to the frequency of large rainfall events under current and future (pseudo-global warming) climate scenarios. This simple analysis allowed us to map the potential risk of excess rainfall across the main Hawaiian Islands. Here we provide rasters that contain the probability of rainfall exceeding infiltration capacity in each grid cell at 90 m. We have included rasters of excess rainfall probabilities for current (2002-2012) and future (2090-2099) scenarios as well as by each individual land cover class considered.