AI-based temporal downscaling of global climate data for improving heatwave characterization over Denmark
Background Global climate datasets (reanalyses or CMIP simulations) are commonly available as monthly means and daily aggregates (mean, max., and min.) for key atmospheric variables. In this study, we compiled such temporal low-resolution data from the ERA5 [1] reanalysis, using high-resolution hourly data from the CERRA [2] regional reanalysis as ground truth Objectives We address the challenge of temporal downscaling (generate high-resolutuion climate predictions) by fine-tuning foundational AI weather models, such as GenCast [3], to transform coarse monthly/daily climate inputs into high-resolution hourly fields. This approach enables a physically consistent reconstruction of the diurnal cycle and peak intensity of heatwaves over Denmark. ...