GDNat: a global, daily, high-resolution, natural-forcing only temperature data set for attribution research

Attribution studies are crucial for understanding the anthropogenic contributions to meteorological extremes and have com-
monly relied on approaches that compare historical observations with global climate model (GCM) simulations that are driven
solely by natural forcing (Bindoff et al. (2013)). However, GCM simulations have limited spatial resolution and are biased by
parameterized climate processes and uninitialized conditions that lead to the lack of representation of historical meteorological
events (Cannon et al. (2015); Eyring et al. (2016); Almazroui (2021); Zhang et al. (2023)). We address these gaps by devel-
oping a novel, high-resolution dataset that provides daily average global temperatures over the past four decades without the
influence of anthropogenic climate forcing. We use quantile delta mapping (QDM), a quantile trend-preserving bias adjust-
ment method, to remove anthropogenic warming from the fifth generation of the European Centre for Medium-Range Weather
Forecasts Reanalysis (ERA5) using historical and natural-forcing-only simulations from the Coupled Model Intercomparison
Project Phase 6 (CMIP6). The resulting dataset consists of historical Global Daily Natural temperature (henceforth, GDNat)
records at 0.25^◦ x 0.25^◦ spatial resolution from 1979 - 2020, providing a valuable resource for attributing extremes and their
impacts to anthropogenic warming.