Output data: Varying sources of uncertainty in risk-relevant hazard projections across the United States

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Abstract Physical climate risk assessment requires understanding how different sources of uncertainty affect hazard projections. However, the relative importance of these uncertainties can differ across end-uses. Here, we combine three state-of-the-art downscaled climate model ensembles to characterize how different uncertainties affect projections of several temperature- and precipitation-based risk metrics across the contiguous United States. We focus on long-term trends of aggregate indices as well as the intensity of rare events with 10- to 100-year return periods. By including new downscaled initial condition ensembles, we characterize the role and relative importance of internal variability at local scales. Our results demonstrate systematic differences in patterns of uncertainty between average and extreme indices, across recurrence intervals, and between temperature- and precipitation-derived metrics. We show that temperature metrics are more sensitive to the choice of emissions scenario and Earth system model, while internal variability can be dominant for precipitation-based metrics. Additionally, we find that the statistical uncertainty from extreme value distribution fitting can often exceed climate-related factors, particularly at recurrence intervals of 50 years or longer. These results highlight the challenge of providing general guidance for climate impacts assessment and the need to consider a wide variety of potential uncertainties when quantifying climate risk. Journal reference Currently undergoing peer review: https://doi.org/10.22541/essoar.15003332/v1 Data description - `ensemble_summary` contains the ensemble means and upper/lower quantiles for each downscaling method and SSP combination; the trend metrics are grouped into a single netcdf file while the return level outputs are separated by variable. - `trends` contains the individual trend estimates for each member of the meta-ensemble; there are separate files for each variable and for each sub-ensemble (i.e., downscaling method). - `GEV` contains the GEV outputs, including the GEV parameter estimates and calculated associated return levels; there separate files for the GEV parameters and return levels, and for each variable and sub-ensemble (i.e., downscaling method). - `uncertainty_results` contains our main uncertainty decomposition results; the trend metrics are grouped into a single netcdf file while the return level outputs are separated by variable. Notes: - NetCDF files are compressed via zlib so may take longer than expected to open, especially the trend and GEV estimates that contain outputs for all individual ensemble members - To ensure maximal compatibility, strings coordinates (e.g. for SSPs or ESMs) are encoded as fixed-length byte strings (NetCDF4 char arrays); python users can use `ds[coord].astype(str)` to convert after loading. - All trend and GEV calculations were performed on the native grids of the downscaled outputs, then regridded to the LOCA2 grid using a nearest neighbors algorithm. All uncertainty results are valid over CONUS only. Contact Additional details can be found in the preprint (https://doi.org/10.22541/essoar.15003332/v1) or corresponding GitHub repository (https://github.com/david0811/conus_comparison_lafferty-etal-2026). Email: dcl257@cornell.edu

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