Global climate models are important tools for understanding the climate system and how it is projected to evolve under scenario-driven emissions pathways. Their output is widely used in climate impacts research for modeling the current and future effects of climate change. However, climate model output remains coarse in relation to the high-resolution climate data needed for climate impacts studies, and it also exhibits biases relative to observational data. Treatment of the distribution tails is a key challenge in existing downscaled climate datasets available at a global scale; many of these datasets used quantile mapping techniques that were known to dampen or amplify trends in the tails.
In this study, we apply the trend-preserving Quantile Delta Mapping (QDM) bias-adjustment method (Cannon et al., 2015) and develop a new downscaling method called the Quantile-Preserving Localized-Analog Downscaling (QPLAD) method that also preserves trends in the distribution tails. Both methods are integrated into a transparent and reproducible software pipeline, which we apply to global, daily model output for surface variables (maximum and minimum temperature and total precipitation) from the Coupled Model Intercomparison Project Phase 6 (CMIP6) experiments (O’Neill et al., 2016) for the historical experiment and four future emissions scenarios ranging from aggressive mitigation to no mitigation: SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 (Riahi et al., 2017). We use European Centre for Medium-RangeWeather Forecasts (ECMWF) ERA5 (Hersbach et al., 2018) temperature and precipitation reanalysis data as the reference dataset over the Sixth Intergovernmental Panel on Climate Change (IPCC) Assessment Report (AR6) reference period, 1995–2014. We produce bias-adjusted and downscaled data over the historical period (1950–2014) and for four emissions pathways (2015–2100) for 25 models in total.
The output dataset of this study is the Global Downscaled Projections for Climate Impacts Research (GDPCIR), a global, daily, 0.25° horizontal-resolution product which is publicly hosted on Microsoft AI for Earth’s Planetary Computer (https://planetarycomputer.microsoft.com/dataset/group/cil-gdpcir/).