Supplement that indicates where to find the source data sets on the EPA system. This dataset is associated with the following publication: Bowden, J., K.D. Talgo, T. Spero , and C. Nolte. Assessing the Added Value of Dynamical Downscaling Using the Standardized Precipitation Index. ADVANCES IN METEOROLOGY. Hindawi Publishing Corporation, New York, NY, USA, 2016(8432064): 14 pages, (2016).

We have completed an array of high-resolution simulations of present and future climate over Western North America (WNA) and Eastern North America (ENA) by dynamically downscaling global climate simulations using a regional climate model, RegCM3. The simulations are intended to provide long time series of internally consistent surface and atmospheric variables for use in climate-related research. In addition to providing high-resolution weather and climate data for the past, present, and future, we have developed an integrated data flow and methodology for processing, summarizing, viewing, and delivering the climate datasets to a wide range of potential users. Our simulations were run over 50- and 15-kilometer model grids in an attempt to capture more of the climatic detail associated with processes such as topographic forcing than can be captured by general circulation models (GCMs). The simulations were run using output from four GCMs. All simulations span the present (for example, 1968 to 1999), common periods of the future (2040 to 2069), and two simulations continuously cover 2010 to 2099. The trace gas concentrations in our simulations were the same as those of the GCMs: the IPCC 20th century time series for 1968 to 1999 and the A2 time series for simulations of the future. We demonstrate that RegCM3 is capable of producing present day annual and seasonal climatologies of air temperature and precipitation that are in good agreement with observations. Important features of the high-resolution climatology of temperature, precipitation, snow water equivalent (SWE), and soil moisture are consistently reproduced in all model runs over WNA and ENA. The simulations provide a potential range of future climate change for selected decades and display common patterns of the direction and magnitude of changes. As expected, there are some model to model differences that limit interpretability and give rise to uncertainties. Here, we provide background information about the GCMs and the RegCM3, a basic evaluation of the model output and examples of simulated future climate. We also provide information needed to access the web applications for visualizing and downloading the data, and give complete metadata that describe the variables in the datasets.

Global climate models (GCMs) are computationally intensive, physics-based research tools used to simulate the climate system. GCM can also be useful in applied research contexts with the use of statistical downscaling techniques. This collection of statistically downscaled climate projections includes 7 sets of SD-processed CMIP5 projections and 12 sets of SD-processed CMIP6 projections of daily high temperature, daily low temperature, and daily total precipitation across the Edwards Aquifer Region (EAR) in south central Texas. These sets of projections were created using four GCMs from the CMIP5 archive (CMCC-CM, HadGEM2-CC, inmcm4, MRI-ESM1) and six GCMs from the CMIP6 archive (EC-Earth3, INM-CM-4-8, INM-CM-5-0, KACE-1-0-G, KIOST-ESM, and MPI-ESM1-2-HR), each of which simulated 21st century climate responses for multiple future emissions scenarios. The CMIP5 GCMs simulated response under the representative concentration pathways (RCPs) 4.5 and 8.5. The CMIP6 GCMs simulated response under the shared socioeconomic pathways (SSPs) 2-4.5 and 5-8.5. The equi-distant quantile mapping method (EDQM) was used for statistical downscaling with the Daymet v. 4 as the observational data used for training. The resulting SD-processed projections are on a 1 km by 1 km grid covering the EAR in south central Texas (100.75 degress E to 97.5 degrees E, 28.75 degrees N to 30.50 degrees N). Both historical baseline files (1980-2005 for CMIP5 and 1980-2014 for CMIP6) and future projections (2006-2100 for CMIP5 and 2015-2100 for CMIP6) are provided. Applied researchers may explore aspects of potential changes in the EAR using these high resolution projections, including as inputs to additional modelling (e.g. hydrology modeling, crop modeling, etc.). This collection should not be considered comprehensive in spanning the entire scope of SD processed climate projections for the EAR. These climate projection data products are provided as is without any warranty and no agreement to support subsequent projects based on this dataset, beyond providing the data to public domain.

Global climate models (GCMs) are computationally intensive, physics-based research tools used to simulate the climate system. GCM can also be useful in applied research contexts with the use of statistical downscaling techniques. This collection of statistically downscaled climate projections includes 7 sets of SD-processed CMIP5 projections and 12 sets of SD-processed CMIP6 projections of daily high temperature, daily low temperature, and daily total precipitation across the Edwards Aquifer Region (EAR) in south central Texas. These sets of projections were created using four GCMs from the CMIP5 archive (CMCC-CM, HadGEM2-CC, inmcm4, MRI-ESM1) and six GCMs from the CMIP6 archive (EC-Earth3, INM-CM-4-8, INM-CM-5-0, KACE-1-0-G, KIOST-ESM, and MPI-ESM1-2-HR), each of which simulated 21st century climate responses for multiple future emissions scenarios. The CMIP5 GCMs simulated response under the representative concentration pathways (RCPs) 4.5 and 8.5. The CMIP6 GCMs simulated response under the shared socioeconomic pathways (SSPs) 2-4.5 and 5-8.5. The equi-distant quantile mapping method (EDQM) was used for statistical downscaling with the Daymet v. 4 as the observational data used for training. The resulting SD-processed projections are on a 1 km by 1 km grid covering the EAR in south central Texas (100.75 degress E to 97.5 degrees E, 28.75 degrees N to 30.50 degrees N). Both historical baseline files (1980-2005 for CMIP5 and 1980-2014 for CMIP6) and future projections (2006-2100 for CMIP5 and 2015-2100 for CMIP6) are provided. Applied researchers may explore aspects of potential changes in the EAR using these high resolution projections, including as inputs to additional modelling (e.g. hydrology modeling, crop modeling, etc.). This collection should not be considered comprehensive in spanning the entire scope of SD processed climate projections for the EAR. These climate projection data products are provided as is without any warranty and no agreement to support subsequent projects based on this dataset, beyond providing the data to public domain.

Global climate models (GCMs) are computationally intensive, physics-based research tools used to simulate the climate system. GCM can also be useful in applied research contexts with the use of statistical downscaling techniques. This collection of statistically downscaled climate projections includes 7 sets of SD-processed CMIP5 projections of daily high temperature, daily low temperature, and daily total precipitation across the Edwards Aquifer Region (EAR) in south central Texas. These sets of projections were created using four GCMs from the CMIP5 archive (CMCC-CM, HadGEM2-CC, inmcm4, MRI-ESM1), each of which simulated 21st century climate responses for multiple future emissions scenarios. The CMIP5 GCMs simulated response under the representative concentration pathways (RCPs) 4.5 and 8.5. The equi-distant quantile mapping method (EDQM) was used for statistical downscaling with the Daymet v. 4 as the observational data used for training. The resulting SD-processed projections are on a 1 km by 1 km grid covering the EAR in south central Texas (100.75 degress E to 97.5 degrees E, 28.75 degrees N to 30.50 degrees N). Both historical baseline files (1980-2005) and future projections (2006-2100) are provided. Applied researchers may explore aspects of potential changes in the EAR using these high resolution projections, including as inputs to additional modelling (e.g. hydrology modeling, crop modeling, etc.). This collection should not be considered comprehensive in spanning the entire scope of SD processed climate projections for the EAR. These climate projection data products are provided as is without any warranty and no agreement to support subsequent projects based on this dataset, beyond providing the data to public domain.

Global climate models (GCMs) are computationally intensive, physics-based research tools used to simulate the climate system. GCM can also be useful in applied research contexts with the use of statistical downscaling techniques. This collection of statistically downscaled climate projections includes 12 sets of SD-processed CMIP6 projections of daily high temperature, daily low temperature, and daily total precipitation across the Edwards Aquifer Region (EAR) in south central Texas. These sets of projections were created using six GCMs from the CMIP6 archive (EC-Earth3, INM-CM-4-8, INM-CM-5-0, KACE-1-0-G, KIOST-ESM, and MPI-ESM1-2-HR), each of which simulated 21st century climate responses for multiple future emissions scenarios. The CMIP6 GCMs simulated response under the shared socioeconomic pathways (SSPs) 2-4.5 and 5-8.5. The equi-distant quantile mapping method (EDQM) was used for statistical downscaling with the Daymet v. 4 as the observational data used for training. The resulting SD-processed projections are on a 1 km by 1 km grid covering the EAR in south central Texas (100.75 degress E to 97.5 degrees E, 28.75 degrees N to 30.50 degrees N). Both historical baseline files (1980-2014) and future projections (2015-2100) are provided. Applied researchers may explore aspects of potential changes in the EAR using these high resolution projections, including as inputs to additional modelling (e.g. hydrology modeling, crop modeling, etc.). This collection should not be considered comprehensive in spanning the entire scope of SD processed climate projections for the EAR. These climate projection data products are provided as is without any warranty and no agreement to support subsequent projects based on this dataset, beyond providing the data to public domain.

Global climate models (GCMs) are computationally intensive, physics-based research tools used to simulate the climate system. GCM can also be useful in applied research contexts with the use of statistical downscaling techniques. This collection of statistically downscaled climate projections includes 12 sets of SD-processed CMIP6 projections of daily high temperature, daily low temperature, and daily total precipitation across the Edwards Aquifer Region (EAR) in south central Texas. These sets of projections were created using six GCMs from the CMIP6 archive (EC-Earth3, INM-CM-4-8, INM-CM-5-0, KACE-1-0-G, KIOST-ESM, and MPI-ESM1-2-HR), each of which simulated 21st century climate responses for multiple future emissions scenarios. The CMIP6 GCMs simulated response under the shared socioeconomic pathways (SSPs) 2-4.5 and 5-8.5. The equi-distant quantile mapping method (EDQM) was used for statistical downscaling with the Daymet v. 4 as the observational data used for training. The resulting SD-processed projections are on a 1 km by 1 km grid covering the EAR in south central Texas (100.75 degress E to 97.5 degrees E, 28.75 degrees N to 30.50 degrees N). Both historical baseline files (1980-2014) and future projections (2015-2100) are provided. Applied researchers may explore aspects of potential changes in the EAR using these high resolution projections, including as inputs to additional modelling (e.g. hydrology modeling, crop modeling, etc.). This collection should not be considered comprehensive in spanning the entire scope of SD processed climate projections for the EAR. These climate projection data products are provided as is without any warranty and no agreement to support subsequent projects based on this dataset, beyond providing the data to public domain.

We have completed an array of high-resolution simulations of present and future climate over Western North America (WNA) and Eastern North America (ENA) by dynamically downscaling global climate simulations using a regional climate model, RegCM3. The simulations are intended to provide long time series of internally consistent surface and atmospheric variables for use in climate-related research. In addition to providing high-resolution weather and climate data for the past, present, and future, we have developed an integrated data flow and methodology for processing, summarizing, viewing, and delivering the climate datasets to a wide range of potential users. Our simulations were run over 50- and 15-kilometer model grids in an attempt to capture more of the climatic detail associated with processes such as topographic forcing than can be captured by general circulation models (GCMs). The simulations were run using output from four GCMs. All simulations span the present (for example, 1968 to 1999), common periods of the future (2040 to 2069), and two simulations continuously cover 2010 to 2099. The trace gas concentrations in our simulations were the same as those of the GCMs: the IPCC 20th century time series for 1968 to 1999 and the A2 time series for simulations of the future. We demonstrate that RegCM3 is capable of producing present day annual and seasonal climatologies of air temperature and precipitation that are in good agreement with observations. Important features of the high-resolution climatology of temperature, precipitation, snow water equivalent (SWE), and soil moisture are consistently reproduced in all model runs over WNA and ENA. The simulations provide a potential range of future climate change for selected decades and display common patterns of the direction and magnitude of changes. As expected, there are some model to model differences that limit interpretability and give rise to uncertainties. Here, we provide background information about the GCMs and the RegCM3, a basic evaluation of the model output and examples of simulated future climate. We also provide information needed to access the web applications for visualizing and downloading the data, and give complete metadata that describe the variables in the datasets.

LOCA is a statistical downscaling technique that uses past history to add improved fine-scale detail to global climate models. We have used LOCA to downscale 32 global climate models from the CMIP5 archive at a 1/16th degree spatial resolution, covering North America from central Mexico through Southern Canada. The historical period is 1950-2005, and there are two future scenarios available: RCP 4.5 and RCP 8.5 over the period 2006-2100 (although some models stop in 2099). The variables currently available are daily minimum and maximum temperature, and daily precipitation. For more information visit: http://loca.ucsd.edu/