A statistical approach on rapid estimations of climate change indices by monthly instead of daily data

Abstract

Climate change indices (CCI) defined by the expert team on climate change detection and indices (ETCCDI) profoundly contribute to understanding climate and its change. They are used to present climate change in an easy to understand and tangible way, thus facilitating climate communication. Many of the indices are peak over threshold indices needing daily and, if necessary, bias corrected data to be calculated from. We present a method to rapidly estimate specific CCI from monthly data instead of daily while also performing a simple bias correction as well as a localisation (downscaling). Therefore, we used the ERA5 Land data with a spatial resolution of 0.1° supplemented by a CMIP6 ssp5-8.5 climate projection to derive different regression functions which allow a rapid estimation by monthly data. Using a climate projection as a supplement in training the regression functions allows an application not only on historical periods but also on future periods such as those provided by climate projections. Nevertheless, the presented method can be adapted to any data set, allowing an even higher spatial resolution.