In this study, a set of 22 sector-relevant extreme temperature indices, approved by the Expert Team on Climate Risk and Sector-specific Indices (ET-SCI), was calculated from daily values of maximum and minimum temperatures from the gridded data of the ENSEMBLES-RT5 European project (E-OBS, ensemble version 17.e), at the location of 5 Southern European metropolitan cities: Athens, Barcelona, Lisbon, Marseille and Naples.Daily Maximum (TX) and minimum (TN) surface temperature data were extracted from the gridded Europe-wide ensemble dataset (E-OBS), from the EU-FP6 project UERRA (http://www.uerra.eu) and the Copernicus Climate Change Service, based on station observations data provided by the European Climate Assessment & Dataset project (ECA&D, available at https://www.ecad.eu) (Cornes et al. 2018). Maximum temperature (TX) and Minimum Temperature (TN) time series were extracted at the location of each city, assuring that each grid cell contained an ECA&D station, thus reducing uncertainty to the minimum. The whole time span available at the time of the study was considered (from 1950 up to September 2018), and only Athens had fewer data available, with time series ending on 2005. The study focuses on 22 sector-relevant extreme temperature indices, from the Expert Team on Climate Risk and Sector-specific Indices (ET-SCI), all calculated on a yearly basis, using the R-based ClimPACT2 tool (Alexander and Herold 2016).The time series data was subject to quality control (QC), the first step in using the ClimPACT2 software, as instructed in the respective User Guide (Alexander and Herold 2016). After QC assessment, reported errors were manually checked, and replaced with null values (missing data). Afterwards, missing data percentage was assessed, assuring it to comply with the World Meteorological Organization recommendations (WMO 2016): missing data percentage was below the 0.05% level, on every case.Please note that the data here presented corresponds to an earlier version of the dataset mentioned in the following articles: Oliveira et al. (2022a: doi:10.1016/j.dib.2022.108511) and Oliveira et al. (2022b: doi:10.1016/j.wace.2022.100455).

We acknowledge the E-OBS dataset from the EU-FP6 project UERRA (http://www.uerra.eu) and the Copernicus Climate Change Service, and the data providers in the ECA&D project (https://www.ecad.eu).