HadEX-CAM dataset: original and deep learning infilled TX90p, TN90p, TX10p, TN10p ETCCDI Indices (CLINT H2020)

Project: Climate Intelligence - Tropical cyclones, heatwaves and extreme droughts are examples of extreme climate events that are difficult to predict. Climate change has increased the likelihood and severity of such events, and predicting their occurrence is essential but difficult. The EU-funded CLINT project (https://climateintelligence.eu/) draws from data collected by the Copernicus Climate Change Service and from recent advances in artificial intelligence (AI). By applying an AI framework composed of machine learning techniques and algorithms, it processes big climate datasets for improving climate science in terms of detection, causation, and attribution of extreme events. CLINT also covers extreme events' quantification impacts on various socio-economic sectors at the pan-European scale and at the local scale in different types of climate change hotspots.
Summary: 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.