ClimTip-GML: A global bias-corrected and downscaled dataset for assessing impacts of climate tipping events

Assessing the impacts of future climate scenarios including tipping events of major Earth system components such as the Amazon rainforest (ARF) or the Atlantic meridional overturning circulation (AMOC), requires accurate and high-resolution simulations. Here, we present ClimTip-GML, the first globally bias-corrected and downscaled climate dataset for impact assessment of large-scale tipping scenarios, comprising eight key variables at 0.25° spatial resolution from three general circulation models (GCMs): CESM1-CAM5, HadGEM3-GC31-MM, and MPI-ESM1-2-HR. The dataset includes 100-year-long climate simulations with preindustrial and historical conditions, as well as scenarios at a +2°C warming level with and without tipping transitions of the AMOC or ARF. We apply generative machine learning (GML) techniques trained on reanalysis data to bias-correct and downscale the GCMs in a manner that is physically consistent across space, time, and all eight variables. Comprehensive validation shows substantially reduced biases, improved small-scale spatial variability, multivariate correlations, and consistent long-term climate responses to the external forcing and tipping events. The results hence permit substantially improved impact assessments of tipping transitions of the ARF and AMOC, directly informing mitigation and adaptation policies.