This dataset was assembled using statistically downscaled climate projections from the NASA Earth Exchange-Global Daily Downscaled Projections (NEX-GDDP) project. These climate change scenarios have been developed using global climate models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6) and four different future scenarios, known as Shared Socioeconomic Pathways (SSPs). The four SSPs involved in this project are SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5. The raw NEX-GDDP-CMIP6 data has a spatial resolution of 0.25 degrees and a daily temporal resolution. The NEX-GDDP-CMIP6 data was processed to calculate change in climatic variables for each season (fall, spring, summer, winter) and annually for 30-year periods. The five period comparisons available in the dataset are as follows: 1976-2005 to 2025-2054, 1976-2005 to 2045-2074, 1976-2005 to 2070-2099, 2025-2054 to 2045 to 2074, and 2025-2054 to 2070 to 2099. The six climatic variables included in the dataset are change in: total precipitation [in], total precipitation [%], total potential evapotranspiration [in], total potential evapotranspiration [%], maximum temperature [degF], and minimum temperature [degF]. This data was then used to produce an ensemble median of all available NEX-GDDP downscaled GCMs for each variable. Not all GCMs downscaled in NEX-GDDP had availability for every variable and scenario combination. The ensemble data was summarized by HUC-12 feature classes described above. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheets (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

This annual data was accessed from the PRISM project website (https://prism.oregonstate.edu/normals_other/public/pacisl/grids/) and has a spatial resolution of 3 arcsec (80 m). The three climatic variables included in the dataset are total precipitation (inches), maximum temperature (degrees Fahrenheit), and minimum temperature (degrees Fahrenheit). PRISM Climate Group at Oregon State University used climate observations from monitoring stations and interpolated to a gridded format using the PRISM model (Parameter-elevation Regressions on Independent Slopes Model). Interpolation was trained using a DEM (digital elevation model) to improve performance in mountainous regions. The PRISM temperature data were originally reported in °C but were converted to °F. The PRISM precipitation data were originally reported in millimeters but were converted to inches. For this project, Guam and CNMI domains were merged into one spatial domain using the "Mosaic to New Raster" tool in ArcGIS Pro. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets). From Original PRISM Metadata (https://prism.oregonstate.edu/normals_other/public/pacisl/metadata/): Abstract: This data set contains spatially gridded average monthly and annual precipitation and temperature for the climatological period 1971–2000. Distribution of the point measurements to a spatial grid was accomplished using the PRISM model, developed by Chris Daly of the PRISM Group, OSU. Purpose: Display and/or analyses requiring spatially distributed monthly or annual precipitation and temperature for the climatological period 1971–2000 Supplementary Information: There are many methods of interpolating climate from monitoring stations to grid points. Some provide estimates of acceptable accuracy in flat terrain, but few have been able to adequately explain the extreme, complex variations in climate that occur in mountainous regions. Significant progress in this area has been achieved through the development of PRISM (Parameter-elevation Regressions on Independent Slopes Model). PRISM is an analytical model that uses point data for a 30 yr climatological average (e.g. 1971- 2000 average) and an underlying grid such as a digital elevation model (DEM) to generate gridded estimates of monthly and annual precipitation and temperature (as well as other climatic parameters). PRISM is well suited to regions with mountainous terrain, because it incorporates a conceptual framework that addresses the spatial scale and pattern of orographic processes. Grids were modeled on a monthly basis. Annual grids of temperature were produced by averaging the monthly grids, and summing for precipitation. Reports and papers on PRISM can be obtained from the PRISM Group website Completeness Report: Point estimates of precipitation and temperature originated from some or all of the following sources: 1) National Weather Service (NWS) Cooperative (COOP) stations, 2) Natural Resources Conservation Service (NRCS) SNOTEL, 3) United States Forest Service (USFS) and Bureau of Land Management (BLM) RAWS Stations, 4) Bureau of Reclemation (AGRIMET) stations, 5) California Data Exchange Center (CDEC) stations, 6) Storage guages, 7) NRCS Snowcourse stations, 8) Other State and local station networks, 9) Estimated station data, 10) Canadian stations, 11) Upper air stations, and 12) NWS/Federal Aviation Administration (FAA) Automated surface observation

This dataset was assembled using statistically downscaled climate projections from the NASA Earth Exchange-Global Daily Downscaled Projections (NEX-GDDP) project. These climate change scenarios have been developed using global climate models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6) and four different future scenarios, known as Shared Socioeconomic Pathways (SSPs). The four SSPs involved in this project are SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5. The raw NEX-GDDP-CMIP6 data has a spatial resolution of 0.25 degrees and a daily temporal resolution. The NEX-GDDP-CMIP6 data was processed to calculate change in climatic variables for each season (fall, spring, summer, winter) and annually for 30-year periods. The five period comparisons available in the dataset are as follows: 1976-2005 to 2025-2054, 1976-2005 to 2045-2074, 1976-2005 to 2070-2099, 2025-2054 to 2045 to 2074, and 2025-2054 to 2070 to 2099. The six climatic variables included in the dataset are change in: total precipitation [in], total precipitation [%], total potential evapotranspiration [in], total potential evapotranspiration [%], maximum temperature [degF], and minimum temperature [degF]. This data was then used to produce an ensemble median of all available NEX-GDDP downscaled GCMs for each variable. Not all GCMs downscaled in NEX-GDDP had availability for every variable and scenario combination. The ensemble data was summarized by HUC-12 feature classes described above. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheets (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

This dataset was assembled using statistically downscaled climate projections from the NASA Earth Exchange-Global Daily Downscaled Projections (NEX-GDDP) project. These climate change scenarios have been developed using global climate models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6) and four different future scenarios, known as Shared Socioeconomic Pathways (SSPs). The four SSPs involved in this project are SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5. The raw NEX-GDDP-CMIP6 data has a spatial resolution of 0.25 degrees and a daily temporal resolution. The NEX-GDDP-CMIP6 data was processed to calculate change in climatic variables for each season (fall, spring, summer, winter) and annually for 30-year periods. The five period comparisons available in the dataset are as follows: 1976-2005 to 2025-2054, 1976-2005 to 2045-2074, 1976-2005 to 2070-2099, 2025-2054 to 2045 to 2074, and 2025-2054 to 2070 to 2099. The six climatic variables included in the dataset are change in: total precipitation [in], total precipitation [%], total potential evapotranspiration [in], total potential evapotranspiration [%], maximum temperature [in], and minimum temperature [degF]. This data was then used to produce an ensemble median of all available NEX-GDDP downscaled GCMs for each variable. Not all GCMs downscaled in NEX-GDDP had availability for every variable and scenario combination. The ensemble data was summarized by HUC-12 feature classes described above. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheets (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

This EnviroAtlas dataset contains projected change in 30-year normals in maximum temperature, minimum temperature, evapotranspiration, and precipitation, for Alaska, Hawaii, Puerto Rico, the U.S. Virgin Islands, American Samoa, Guam and Commonwealth of the Northern Mariana Islands. This dataset was created using the NASA Earth Exchange-Global Daily Downscaled Projections (NEX-GDDP), developed using Global Climate Models (GCMs) runs conducted under the Coupled Model Intercomparison Project Phase 6 (CMIP6) across four scenarios, known as Shared Socioeconomic Pathways (SSPs). The four SSPs involved in this project are SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5. The raw NEX-GDDP-CMIP6 data has a spatial resolution of 0.25 degrees and a daily temporal resolution. The NEX-GDDP-CMIP6 data was processed to calculate projected changes in six climatic variables for each season (fall, spring, summer, winter) and annually for five 30-year periods: recent history (1976-2005) to near-term future (2025-2054) recent history (1976-2005) to mid-century (2045-2074) recent history (1976-2005) to end-of-century (2070-2099) near-term future (2025-2054) to mid-century (2045 to 2074) near-term future (2025-2054) to end-of-century (2070 to 2099) The six climatic variables included in the dataset are change in: total precipitation [in and fraction] total potential evapotranspiration [in and fraction] maximum temperature [degF], and minimum temperature [degF]. This data was then used to produce an NEX-GDDP-CMIP6 ensemble median for each variable for each HUC12. Not all GCMs downscaled in NEX-GDDP-CMIP6 had availability for every variable and scenario combination. Due to low historical values in precipitation and potential evapotranspiration, the data for Alaska is not available in fraction unit. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. EnviroAtlas includes a user-friendly interactive map for data discovery, https://enviroatlas.epa.gov/enviroatlas/interactivemap.

This annual data was accessed from the PRISM project website (https://https://prism.oregonstate.edu/normals_other/public/pacisl/grids/) and has a spatial resolution of 3 arcsec (80 m). The three climatic variables included in the dataset are total precipitation (inches), maximum temperature (degrees Fahrenheit), and minimum temperature (degrees Fahrenheit). PRISM Climate Group at Oregon State University used climate observations from monitoring stations and interpolated to a gridded format using the PRISM model (Parameter-elevation Regressions on Independent Slopes Model). Interpolation was trained using a DEM (digital elevation model) to improve performance in mountainous regions. The PRISM temperature data were originally reported in °C but were converted to °F. The PRISM precipitation data were originally reported in millimeters but were converted to inches. For this project, Tutuila and Manua island domains were merged into one spatial domain for American Samoa using the "Mosaic to New Raster" tool in ArcGIS Pro. This dataset was processed by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets). From Original PRISM Metadata (https://prism.oregonstate.edu/normals_other/public/pacisl/metadata/): Abstract: This data set contains spatially gridded average monthly and annual precipitation and temperature for the climatological period 1971–2000. Distribution of the point measurements to a spatial grid was accomplished using the PRISM model, developed by Chris Daly of the PRISM Group, OSU. Purpose: Display and/or analyses requiring spatially distributed monthly or annual precipitation and temperature for the climatological period 1971–2000 Supplementary Information: There are many methods of interpolating climate from monitoring stations to grid points. Some provide estimates of acceptable accuracy in flat terrain, but few have been able to adequately explain the extreme, complex variations in climate that occur in mountainous regions. Significant progress in this area has been achieved through the development of PRISM (Parameter-elevation Regressions on Independent Slopes Model). PRISM is an analytical model that uses point data for a 30 yr climatological average (e.g. 1971- 2000 average) and an underlying grid such as a digital elevation model (DEM) to generate gridded estimates of monthly and annual precipitation and temperature (as well as other climatic parameters). PRISM is well suited to regions with mountainous terrain, because it incorporates a conceptual framework that addresses the spatial scale and pattern of orographic processes. Grids were modeled on a monthly basis. Annual grids of temperature were produced by averaging the monthly grids, and summing for precipitation. Reports and papers on PRISM can be obtained from the PRISM Group website Completeness Report: Point estimates of precipitation and temperature originated from some or all of the following sources: 1) National Weather Service (NWS) Cooperative (COOP) stations, 2) Natural Resources Conservation Service (NRCS) SNOTEL, 3) United States Forest Service (USFS) and Bureau of Land Management (BLM) RAWS Stations, 4) Bureau of Reclemation (AGRIMET) stations, 5) California Data Exchange Center (CDEC) stations, 6) Storage guages, 7) NRCS Snowcourse stations, 8) Other State and local station networks, 9) Estimated station data, 10) Canadian stations, 11) Upper air stations, and 12) NWS/Federal Aviation Administration

This annual data was accessed from the PRISM project website (https://https://prism.oregonstate.edu/normals_other/public/pacisl/grids/) and has a spatial resolution of 15 arcsec (400 m). The three climatic variables included in the dataset are total precipitation (inches), maximum temperature (degrees Fahrenheit), and minimum temperature (degrees Fahrenheit). PRISM Climate Group at Oregon State University used climate observations from monitoring stations and interpolated to a gridded format using the PRISM model (Parameter-elevation Regressions on Independent Slopes Model). Interpolation was trained using a DEM (digital elevation model) to improve performance in mountainous regions. The PRISM temperature data were originally reported in °C but were converted to °F. The PRISM precipitation data were originally reported in millimeters but were converted to inches. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets). From Original PRISM Metadata (https://prism.oregonstate.edu/normals_other/public/pacisl/metadata/): Abstract: This data set contains spatially gridded average monthly and annual precipitation and temperature for the climatological period 1971–2000. Distribution of the point measurements to a spatial grid was accomplished using the PRISM model, developed by Chris Daly of the PRISM Group, OSU. Purpose: Display and/or analyses requiring spatially distributed monthly or annual precipitation and temperature for the climatological period 1971–2000 Supplementary Information: There are many methods of interpolating climate from monitoring stations to grid points. Some provide estimates of acceptable accuracy in flat terrain, but few have been able to adequately explain the extreme, complex variations in climate that occur in mountainous regions. Significant progress in this area has been achieved through the development of PRISM (Parameter-elevation Regressions on Independent Slopes Model). PRISM is an analytical model that uses point data for a 30 yr climatological average (e.g. 1971- 2000 average) and an underlying grid such as a digital elevation model (DEM) to generate gridded estimates of monthly and annual precipitation and temperature (as well as other climatic parameters). PRISM is well suited to regions with mountainous terrain, because it incorporates a conceptual framework that addresses the spatial scale and pattern of orographic processes. Grids were modeled on a monthly basis. Annual grids of temperature were produced by averaging the monthly grids, and summing for precipitation. Reports and papers on PRISM can be obtained from the PRISM Group website Completeness Report: Point estimates of precipitation and temperature originated from some or all of the following sources: 1) National Weather Service (NWS) Cooperative (COOP) stations, 2) Natural Resources Conservation Service (NRCS) SNOTEL, 3) United States Forest Service (USFS) and Bureau of Land Management (BLM) RAWS Stations, 4) Bureau of Reclemation (AGRIMET) stations, 5) California Data Exchange Center (CDEC) stations, 6) Storage guages, 7) NRCS Snowcourse stations, 8) Other State and local station networks, 9) Estimated station data, 10) Canadian stations, 11) Upper air stations, and 12) NWS/Federal Aviation Administration (FAA) Automated surface observation stations (ASOS). All COOP station data were subjected to quality control checks by the National Climatic Data Center (NCDC).

This annual data was accessed from the PRISM project website (https://prism.oregonstate.edu/normals/) and has a spatial resolution of 30 arcsec (800 m). The three climatic variables included in the dataset are total precipitation (inches), maximum temperature (degrees Fahrenheit), and minimum temperature (degrees Fahrenheit). PRISM Climate Group at Oregon State University used climate observations from monitoring stations and interpolated to a gridded format using the PRISM model (Parameter-elevation Regressions on Independent Slopes Model). Interpolation was trained using a DEM (digital elevation model) to improve performance in mountainous regions. The PRISM temperature data were originally reported in °C but were converted to °F. The PRISM precipitation data were originally reported in millimeters but were converted to inches. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets). From Original Metadata (https://prism.oregonstate.edu/normals/): Abstract: Monthly 30-year "normal" dataset covering the conterminous U.S., averaged over the climatological period 1991–2020. Contains spatially gridded average annual total precipitation and temperature at 4km grid cell resolution. Distribution of the point measurements to the spatial grid was accomplished using the PRISM model, developed and applied by Dr. Christopher Daly of the PRISM Climate Group at Oregon State University. This dataset is available free-of-charge on the PRISM website. Purpose: Display and/or analysis requiring spatially distributed annual total precipitation and temperature for the climatological period 1991–2020. Supplementary Information: There are many methods of interpolating climate from monitoring stations to grid points. Some provide estimates of acceptable accuracy in flat terrain, but few have been able to adequately explain the extreme, complex variations in climate that occur in mountainous regions. Significant progress in this area has been achieved through the development of PRISM (Parameter-elevation Regressions on Independent Slopes Model). PRISM is an analytical model that uses point data and an underlying grid such as a digital elevation model (DEM) or a 30 yr climatological average to generate gridded estimates of monthly or annual precipitation and temperature (as well as other climatic parameters). PRISM is well suited to regions with mountainous terrain, because it incorporates a conceptual framework that addresses the spatial scale and pattern of orographic processes. Grids were modeled on a monthly basis. Annual grids were produced by averaging (temperatures, dew point, vapor pressure deficit, and solar radiation) or summing (precipitation) the monthly grids. These gridded normals supersede the 1991–2020 normals produced in October 2021. Improvements over the previous version include more stable adjustments of short-period station data to represent the 1991–2020 period, and additional quality control measures.

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.

This dataset consists of rasters representing downscaled climate change scenarios (8 km resolution) for the Torres Strait and Papua New Guinea regions for 1990, 2055, 2090. This includes estimated mean surface relative humidity (%), wind speed, rainfall rate (mm per day) and surface temperature (degrees Celsius) estimated from simulated conditions for 1980?1999, 2046-2065 and 2080?2099 time periods. Also included is the relative change of each attribute with respect to 1990. For the past decade the Conformal Cubic Atmospheric Model (CCAM) has been the mainstay of CSIRO dynamical downscaling (McGregor 1996, 2005a, 2005b; McGregor and Dix 2001, 2008). CCAM is an atmospheric GCM formulated on the conformal-cubic grid. CCAM includes a fairly comprehensive set of physical parameterizations. The GFDL parameterizations for long-wave and short-wave radiation (Schwarzkopf and Fels 1991; Lacis and Hansen 1974) are employed, with interactive cloud distributions determined by the liquid and ice-water scheme of Rotstayn (1997). The model employs a stability-dependent boundary layer scheme based on Monin-Obukhov similarity theory (McGregor et al. 1993), together with the non-local treatment of Holtslag and Boville (1993). A canopy scheme is included, as described by Kowalczyk et al. (1994), having six layers for soil temperatures, six layers for soil moisture (solving Richard's equation) and three layers for snow. The cumulus convection scheme uses a mass-flux closure, as described by McGregor (2003), and includes downdrafts, entrainment and detrainment. CCAM is not only used for climate studies (Nguyen et al. 2011), it is also used in a short-range weather forecast system (Landman et al. 2012). Methods: All primary simulations were completed using CSIRO’s global stretched-grid, Conformal Cubic Atmospheric Model (CCAM; McGregor and Dix, 2008) run at 60 km horizontal resolution over the entire globe, while further downscaling to 8 km was conducted for selected partner countries. The CCAM model was chosen for the downscaling because it is a global atmospheric model, so it was possible to bias-adjust the sea-surface temperature in order to improve upon large-scale circulation patterns. In addition, the use of a stretched grid eliminates the problems caused by lateral boundary conditions in limited-area models. The model has been well tested in various model inter-comparisons and in downscaling projects over the Australasian region (Corney et al., 2010). CCAM 60 km Global simulations: These simulations were performed for six host global climate models (CSIRO?Mk3.5, ECHAM/MPI?OM, GFDL-CM2.0, GFDL?CM2.1, MIROC3.2 (medres) and UKMO?HadCM3) that were deemed to have acceptable skill in simulating the climate of the Pacific Climate Change Science Program region. The period 1961-2099 was simulated for the A2 (high) emissions scenario only. In these simulations, the sea-surface temperature bias?adjustment was calculated by computing the monthly average biases of the global models for the 1971-2000 period, relative to the observed climatology, based upon the method of Reynolds (1988). These monthly biases were then subtracted from the global climate model monthly sea-surface temperature output throughout the simulation. This approach preserves the inter- and intra-annual variability and the climate change signal of the host global climate models. CCAM 8 km Global simulations: Due to computational cost, only three of the CCAM 60 km global simulations (those using SSTs from GFDL-CM2.1, UKMO-HadCM3 and ECHAM5) were selected for further downscaling to 8 km. Of the six host models, these three GCM simulations showed a low, middle and high amount of global warming into the future, respectively. A scale-selective digital filter developed by Thatcher and McGregor (2009) was used to impose the broad-scale (scales greater than approximately 500 km) fields of temperature, moisture and winds above pressure-sigma level .9 (about 1 km above the surface) from the 60