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Structural Similarity Assessment of Precipitation and Temperature in NEX-GDDP-CMIP6 Simulations over the Contiguous United States
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DOI：10.1175/JHM-D-25-0163.1.png)
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Abstract
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In a warming climate, spatiotemporal changes in precipitation and temperature can impact hydrologic processes and thus the quality of freshwater ecosystems. This study evaluates the performance of 35 downscaled and bias corrected Coupled Model Intercomparison Project phase 6 (CMIP6) general circulation models (GCMs) from NASA's Earth Exchange Global Daily Downscaled Projections (NEX-GDDP, 0.258 resolution) by comparing their historical simulations (1950-2014) of precipitation and near-surface air temperature with those from the fifth generation European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalysis (ERA5) reanalysis across the contiguous United States. Two complementary metrics are employed: the Wasserstein distance (WD) to assess distributional similarity and the structural similarity index measure (SSIM) to quantify spatial consistency on a monthly scale. The results reveal systematic uncertainties: The NEX-GDDP-CMIP6 simulations struggle to reproduce the warm-season spatial organization and temporal trends of precipitation in regions predominantly influenced by mesoscale convective systems. Additionally, they exhibit the lowest SSIM scores for cold-season temperatures, highlighting challenges in simulating snow-albedo feedback mechanisms, cloud-radiative processes, and boundary layer dynamics. The models are ranked based on the similarity of their historical simulations for both precipitation and temperature. In summary, IPSL-CM6A-LR leads in monthly distributional proximity, while MIROC-ES2L leads in monthly spatial similarity for total precipitation and mean temperature. Regionally, MIROC-ES2L performs best in the Midwest, Northern Great Plains, Northwest, and Southern Plains, whereas NorESM2-LM, FGOALS-g3, and GFDL-CM4-gr2 lead in the Northeast, Southeast, and Southwest, respectively. The spatial similarity analysis of maps of temporal trends reveals that BCC-CSM2-MR is the top model across contiguous United States (CONUS).
Keywords:
Mesoscale systems
Hydrology
Climate models
Reanalysis data
Decision making
Water resources
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