Bias-corrected daily precipitation at 1-kilometer resolution for Puerto Rico from Weather Research and Forecasting (WRF) dynamical downscaled historical (1985-2005) and projected (2040-60) climate

This data release consists of four network Common Data Form (netCDF) files of bias-corrected daily precipitation at 1-kilometer (km) scale for historical and projected climate of Puerto Rico. The Weather Research and Forecasting (WRF) model was used by Bowden and others (2018) to downscale two general circulation models (GCMs) from the Coupled Model Intercomparison Project phase 5 (CMIP5): (1) the Community Climate System Model (CCSM4 or CESM), and (2) the Centre National de Recherches Meteorologiques-CERFACS (CNRM). The two models were dynamically downscaled using WRF for a historical (1985-2005) time slice and a future time slice (2040–60) under the high greenhouse gas emission scenario known as Representative Concentration Pathway 8.5 (RCP8.5). Total hourly precipitation data (convective plus non-convective) at 2-km resolution for the innnermost domain in Bowden and others (2018; their domain 3) was aggregated to a daily timestep and then bias-corrected using Multiplicative Quantile Delta Mapping (MQDM; Cannon and others, 2015) with Daymet v4 as the observational gridded precipitation dataset (Thornton and others, 2020). The bias-corrected daily precipitation data was interpolated to the 1-km grid of the Puerto Rico water and energy balance model (GOES-PRWEB; Harmsen and others, 2021) which uses solar radiation from the GOES satellite. References: Bowden, J., Wootten, A., Terando, A., and Boyles, R., 2018, Weather Research and Forecasting (WRF): Puerto Rico and US Virgin Islands Dynamical Downscaled Climate Change Projections: U.S. Geological Survey data release, https://doi.org/10.5066/F7GB23BW. Cannon, A.J., Sobie, S.R., and Murdock, T.Q., 2015, Bias correction of GCM precipitation by quantile mapping: How well do methods preserve changes in quantiles and extremes?: Journal of Climate, v. 28, no. 17, , p. 6938–6959, https://doi.org/10.1175/JCLI-D-14-00754.1. Harmsen, E.W., Mecikalski, J.R., Reventos, V.J., Álvarez Pérez, E., Uwakweh, S.S., and Adorno García, C., 2021, Water and energy balance model GOES-PRWEB: Development and validation: Hydrology v. 8, no. 113, https://doi.org/10.3390/hydrology8030113. Thornton, M.M., Shrestha, R., Wei, Y., Thornton, P.E., Kao, S., and Wilson, B.E., 2020, Daymet: Daily surface weather data on a 1-km grid for North America, version 4: ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1840.

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