Abstract
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.