The HadEX-CAM dataset contains four land-based extreme indices (TX90p, TN90p, TX10p, TN10p) for the European region. The original dataset (containing missing values) has been created by the MetOffice by aggregating station data using the Climate Anomaly Method (CAM). The infilled version of this dataset has been created by DKRZ by applying a deep learning (DL) model based on U-Net architecture and trained on CMIP6 data (see https://www.nature.com/articles/s41467-024-53464-2). The original HadEX-CAM dataset is distributed under the Open Government Licence: http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/. The DL-infilled HadEX-CAM dataset is distributed under the Creative Commons Attribution 4.0 International license.

ETCCDI indices calculated from two km-scale global models developed within the nextGEMS project (https://nextgems-h2020.eu/): ICON-Sapphire (Hohenegger et al. 2023) and IFS-FESOM (Rackow et al. 2025). The indices are based on the 30-year production simulations of nextGEMS, cycle 4 with a spatial resolution of about 10km (Segura et al. 2025). Here, we provide them in the 29-year period 2021-2049 (as the first year, 2020, is incomplete for IFS), driven by the high-emission pathway SSP3-7.0. The original data and the derived indices are available on the unstructured HEALPix grid (Górski et al. 2005). HEALPix organises data at discrete resolutions or zoom levels. Here, the highest resolved zoom level 9 (about 13km grid spacing corresponding to about 3 million grid cells globally) and the intermediate (“CMIP6-like”) zoom level 6 (about 102km, 50’000 grid cells) are provided. The data were processed by Lukas Brunner (https://orcid.org/0000-0001-5760-4524), using a Climate Data Operators (https://code.mpimet.mpg.de/projects/cdo/embedded/index.html) implementation of the ETCCID indices: code on GitHub (https://doi.org/10.5281/zenodo.15582463). Time-mean plots of all indices are available on Zenodo: https://doi.org/10.5281/zenodo.15613611 If you use the indices, please cite this dataset and the accompanying publication: Brunner L., B. Poschlod, E. Dutra, E. M. Fischer, O. Martius, and J. Sillmann (2025): A global perspective on the spatial representation of climate extremes from km-scale models. Environmental Research Letters, https://doi.org/10.1088/1748-9326/ade1ef