Samba Siva Sai Prasad Thota Katherine Hegewisch Imtiaz Rangwala 20250328 NetCDF Sciencebase US Geological Survey Data Release Samba Siva Sai Prasad Thota (0000-0002-3804-4645), University of Colorado, Boulder Katherine Hegewisch (0000-0002-9539-2929), University of California Merced Imtiaz Rangwala (0000-0002-4313-9374), University of Colorado, Boulder https://doi.org/10.5066/P1SV9SPJ The dataset consists of projections of 1-12 months Standardized Precipitation Evapotranspiration Index (SPEI) between 1950-2099 for the contiguous United States from 20 climate models and 2 emission scenarios at a 4km spatial resolution. The SPEI dataset was developed using the SPEI package in R (Beguería & Vicente-Serrano, 2023). SPEI quantifies standardized departures in the balance between precipitation and potential evapotranspiration (PET) across varying timescales, making it highly suitable for assessing drought and water availability (Vicente-Serrano et al., 2010). Monthly precipitation and PET data were sourced from the MACAv2-METDATA dataset for climate projections between 1950-2099 based on 20 global climate models under RCP 4.5 and RCP 8.5 emission scenarios (Abatzoglou, 2013). Projected SPEI values were calculated relative to the 1981-2020 reference period, with SPEI computed using a log-logistic distribution fitted to the difference between precipitation and PET values. This methodology standardizes SPEI values as z-scores, allowing for comparative evaluations of drought and wetness across different regions and timescales (1 to 12 months). There is very limited data available on projections of drought indices and therefore it is a specific need identified by climate change adaptation practitioners to understand trends in future water availability at multiple timescales and across season, and to quantify the changing nature of extremes in droughts and develop scenarios of drought-related risk in future. SPEI is a specifically relevant drought index identified by the scientific community for climate change applications as it integrates the effects of changes in both temperature and precipitation, and could be used as an indicator for land surface water or soil moisture conditions. Beguería, S., & Vicente-Serrano, S. M. (2023). SPEI: Calculation of the Standardized Precipitation-Evapotranspiration Index. R package version 1.8.1. Available at https://CRAN.R-project.org/package=SPEI. Vicente-Serrano, S. M., et al. (2010). A multi-scalar drought index sensitive to global warming: The Standardized Precipitation Evapotranspiration Index – SPEI. Journal of Climate, 23(7), 1696-1718. Abatzoglou, J. T. (2013). Development of gridded surface meteorological data for ecological applications and modelling. International Journal of Climatology, 33(1), 121–131. University of Colorado Boulder Research Computing. (2023). Alpine. University of Colorado Boulder. DOI: https://doi.org/10.25811/k3w6-pk81. 1950 2099 ground condition Complete None planned -124.7388 -66.9287 49.3960 25.0631 ISO 19115 Topic Category biota USGS Thesaurus climate change precipitation (atmospheric) droughts geospatial datasets None SPEI climate projections aridification potential evapotranspiration USGS Metadata Identifier USGS:672bdd7bd34e16b32e739aff Common geographic areas United States None. Please see 'Distribution Info' for details. None. Users are advised to read the dataset's metadata thoroughly to understand appropriate use and data limitations. Imtiaz Rangwala University of Colorado mailing and physical

Suite S340, NC CASC

Boulder CO 80303 USA (732)277-8231 Imtiaz.Rangwala@colorado.edu Funding: NC CASC (USGS Cooperative Agreement grant #G18AC00325); Input Dataset Credit: MACAv2-METDATA; https://climate.northwestknowledge.net/MACA/ Citation: Abatzoglou J.T. and Brown T.J. A comparison of statistical downscaling methods suited for wildfire applications, International Journal of Climatology (2012), 32, 772-780. https://rmets.onlinelibrary.wiley.com/doi/full/10.1002/joc.2312; This work utilized the Alpine high-performance computing resource at the University of Colorado Boulder. Alpine is jointly funded by the University of Colorado Boulder, the University of Colorado Anschutz, Colorado State University, and the National Science Foundation (award 2201538). R programming software and the Alpine high-performance computing resource at the University of Colorado Boulder. R package version 1.8.1 S. Begeria S. M. Vicente-Serrano 2023 application/service R package version 1.8.1. https://CRAN.R-project.org/package=SPEI Sergio M. Vicente-Serrano Santiago Beguería Juan I. López-Moreno 20100401 publication Journal of Climate vol. 23, issue 7 n/a American Meteorological Society ppg. 1696-1718 https://doi.org/10.1175/2009JCLI2909.1 Spot checking and verification of data and its pattern. The data is based on the input variables from the MACAv2METDATA downscaled climate data and are within expected ranges. N/A. The data is based on the input variables from the MACAv2METDATA downscaled climate data. All of the spatial and temporal domain associated with the input data was included. University of California Mercerd 2024 NetCDF https://climate.northwestknowledge.net/MACA/MACAproducts.php Digital and/or Hardcopy 1950 2099 ground condition MACAv2-METDATA Input variables for CONUS Data Compilation: Compiled historical and future climate data (1950-2099) into a continuous time series for each RCP scenario (RCP 4.5 and RCP 8.5) across all grids in the CONUS region. This step was performed using a shell script to automate data handling and combination on the Alpine high-performance computing resource at the University of Colorado Boulder. Month of download: April 2024. 20240401 SPEI Calculation: Computed the Standardized Precipitation Evapotranspiration Index (SPEI) for each grid cell using the SPEI package in R, applying a log-logistic distribution to standardize the balance between precipitation and potential evapotranspiration across 1–12 month timescales. This computation was also performed using the Alpine high-performance computing resource. Month of SPEI computation: June 2024. 20240601 Data Conversion and Compression: Converted the resulting SPEI data to NetCDF format, utilizing the best available compression to optimize storage and data accessibility. Data upload to THREDDS server. Month of NetCDF file compression: October 2024. 20241001 SPEI is calculated as standardized deviation in “z scores”. Units are dimensionless. These files are in the WGS84 Datum. A 1-month January SPEI data means that the driving variables (Precip and PET) are accumulated (summation not averaging) over the 1 month period in January for the calculations for SPEI; while a 6-month January SPEI has the accumulation between August [previous year] and January [current year]. All SPEI data is monthly (mm:yyyy) although with different timescales of accumulation, i.e., 1-12 months. Thota, S. S. S. P., Hegewisch, K., & Rangwala, I., 2025, Standardized Precipitation Evapotranspiration Index (SPEI) projections for the Contiguous United States based on the CMIP5 MACAv2-METDATA downscaled climate dataset: US Geological Survey Data Release Sciencebase, https://doi.org/10.5066/P1SV9SPJ. GS ScienceBase U.S. Geological Survey mailing address

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Denver CO 80225 United States 1-888-275-8747 sciencebase@usgs.gov Unless otherwise stated, all data, metadata and related materials are considered to satisfy the quality standards relative to the purpose for which the data were collected. Although these data and associated metadata have been reviewed for accuracy and completeness and approved for release by the U.S. Geological Survey (USGS), no warranty expressed or implied is made regarding the display or utility of the data on any other system or for general or scientific purposes, nor shall the act of distribution constitute any such warranty. Digital Data https://doi.org/10.5066/P1SV9SPJ None 20250331 Imtiaz Rangwala University of Colorado mailing and physical

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Boulder CO 80303 USA (732)277-8231 Imtiaz.Rangwala@colorado.edu FGDC Content Standard for Digital Geospatial Metadata FGDC-STD-001-1998