High-resolution climate data is crucial for studying regional climate impacts and extremes, especially in topographically complex regions 1. However, users often face barriers when trying to access and process datasets from multiple sources due to differences in data structure, resolution, grid structure, and naming conventions. ClimXtract is a modular Python toolkit developed to address this challenge. It provides standardized functions for downloading, regridding, and spatially masking multiple climate datasets into a common format compatible with any high-resolution climate dataset for a given regional domain. ClimXtract includes support for variable harmonization (e.g., for temperature and precipitation), interpolation for different grid types, and optional masking to a target domain. It builds upon the libraries xarray 2 and CDO 3, which are widely used in the climate data community, and is designed for domain scientists and non-specialists alike. Together with processed example datasets and Jupyter notebooks, ClimXtract provides the climate community with a reproducible workflow for preparing data for research and downstream applications. While here presented using the ÖKS15 dataset for Austria 4 as an example, ClimXtract can equally be applied to other regions of interest and target formats more generally.
Meindl et al. (Thu,) studied this question.