ClimateBenchPress Data Loader

This repository contains the code to download the datasets for the ClimateBenchPress compression benchmark.

Getting Started

This project uses the uv package manager to handle dependencies. If you don't already have it installed follow the instructions at https://docs.astral.sh/uv/getting-started/installation/.

Next, clone this repository and within the project directory install all the necessary dependencies with:

uv sync
uv pip install -e ".[data]"

Downloading the Data

To download all the data used for the benchmark run the following commands:

uv run python -m climatebenchpress.data_loader.datasets.esa_biomass_cci
uv run python -m climatebenchpress.data_loader.datasets.cams
uv run python -m climatebenchpress.data_loader.datasets.ifs_uncompressed
uv run python -m climatebenchpress.data_loader.datasets.ifs_humidity
uv run python -m climatebenchpress.data_loader.datasets.nextgems
uv run python -m climatebenchpress.data_loader.datasets.cmip6.access_ta
uv run python -m climatebenchpress.data_loader.datasets.cmip6.access_tos

This will download the data into a sub-directory named datasets within this repository. If you want to store the data in a different directory you can use the --basepath=${path/to/dir} command line argument for the scripts which will store the data at ${path/to/dir}/datasets instead.

Citation

If you find this work useful, please consider citing the following paper:

@Article{reichelt2026climatebenchpress,
  AUTHOR = {Reichelt, T. and Tyree, J. and Kl\"ower, M. and Dueben, P. and Lawrence, B. N. and Baker, A. H. and Faghih-Naini, S. and Hoefler, T. and Stier, P.},
  TITLE = {ClimateBenchPress (v1.0): a benchmark for lossy compression of climate data},
  JOURNAL = {Geoscientific Model Development},
  VOLUME = {19},
  YEAR = {2026},
  NUMBER = {13},
  PAGES = {5933--5960},
  URL = {https://gmd.copernicus.org/articles/19/5933/2026/},
  DOI = {10.5194/gmd-19-5933-2026}
}

Funding

ClimateBenchPress has been developed as part of Embed2Scale and ESiWACE3.

Funded by the European Union. This work has received funding from the European High Performance Computing Joint Undertaking (JU) under grant agreement No 101093054 and EU’s Horizon Europe program under grant agreement number 101131841. This work also received funding from UK Research and Innovation (UKRI).