Have a personal or library account? Click to login
ClimXtract: A Python Toolkit for Standardizing High-Resolution Climate Datasets for Regional Domains Cover

ClimXtract: A Python Toolkit for Standardizing High-Resolution Climate Datasets for Regional Domains

Open Access
|Feb 2026

Figures & Tables

Table 1

Overview of climate datasets included in ClimXtract.

DATASETTYPESPATIAL RES.TIME RES.COVERAGE
ÖKS15Model1 kmdailyAustria
SPARTACUSObservation1 kmdailyAustria
EURO-CORDEXModel12.5 kmdailyEurope
E-OBSObservation11 kmdailyEurope
DestinE Climate DTModel5–10 kmhourlyGlobal
ERA5Reanalysis30 kmhourly/dailyGlobal
ERA5-LandReanalysis9 kmhourly/dailyGlobal
jors-14-627-g1.png
Figure 1

Global mean near-surface air temperature averaged for the years 2021 to 2023 as simulated by the ICON model for the DestinE Climate Digital Twin.

jors-14-627-g2.png
Figure 2

Example showing mean near-surface air temperature averaged for the years 2021 to 2023 after (b) regridding using distance-weighted interpolation followed by (c) applying the ÖKS15 spatial mask. Panel (a) shows temperature over the same period from the ÖKS15 dataset that serves as the target grid.

jors-14-627-g3.png
Figure 3

Timeseries showing the near-surface air temperature averaged over the Austrian domain for September 2020. Dashed lines show observation- and reanalysis-based datasets (SPARTACUS, E-OBS, ERA5-Land), solid lines show model simulations.

DOI: https://doi.org/10.5334/jors.627 | Journal eISSN: 2049-9647
Language: English
Submitted on: Sep 18, 2025
|
Accepted on: Feb 11, 2026
|
Published on: Feb 25, 2026
Published by: Ubiquity Press
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year

© 2026 Maximilian Meindl, Luiza Sabchuk, Aiko Voigt, published by Ubiquity Press
This work is licensed under the Creative Commons Attribution 4.0 License.