ClimX: a challenge for extreme-aware climate model emulation

ClimX is a challenge about building fast and accurate machine learning emulators for the NorESM2-MM Earth System Model, with evaluation focused on climate extremes rather than mean climate alone.

Dataset summary

This dataset contains the full-resolution ClimX data in NetCDF-4 format (targets + forcings, depending on split) with a native grid of $192\times 288192\; \backslash times\; 288$ (about $1^{\circ }1^\backslash circ$) resolution. It also contains the lite-resolution version, with a native grid of $12\times 1812\; \backslash times\; 18$ (about ${16}^{\circ }16^\backslash circ$) resolution:

• Lite-resolution: <1GB, $16\times 16\backslash times$ spatially coarsened, meant for rapid prototyping.
• Full-resolution: ~200GB, full-resolution data for large-scale training.

What you will do (high level)

You train an emulator that predicts daily 2D fields for 7 surface variables:

• tas, tasmax, tasmin
• pr, huss, psl, sfcWind

However, the benchmark targets are 15 extreme indices derived from daily temperature and precipitation (ETCCDI-style indices). The daily fields are an intermediate output your emulator produces (useful for diagnostics and for computing the indices).

Conceptually:

$$x_t=g(f_t,f_{t-1},\dots ,f_{t-\alpha },x_{t-1},x_{t-2},\dots ,x_{t-\beta })x\_t\; =\; g(f\_t,\; f\_\{t-1\},\; \backslash dots,\; f\_\{t-\backslash alpha\},\; x\_\{t-1\},\; x\_\{t-2\},\; \backslash dots,\; x\_\{t-\backslash beta\})$$

where $f_{t}f\_t$ are forcings (greenhouse gases + aerosols) and $x_{t}x\_t$ is the climate state.

Dataset structure

Spatial and temporal shape

Full-resolution daily fields:

• Historical: lat: 192, lon: 288, time: 60224
• Projections: lat: 192, lon: 288, time: 31389

Splits and scenarios (official challenge setup)

Training uses historical + several SSP scenarios; testing is on the held-out SSP2-4.5 scenario:

• Train: historical (1850–2014) + ssp126, ssp370, ssp585 (2015–2100)
• Test (held-out): ssp245 (2015–2100)

To avoid leakage, targets for ssp245 are withheld in the official evaluation; only the forcings are provided for that scenario. The full outputs will be released after the competition.

How to load the data

This dataset is distributed as NetCDF-4 files. There are two common ways to load it.

Option 1 (recommended): clone the ClimX code and use the helper loader

The ClimX repository already includes a helper module (src/data/climx_hf.py) that allows you to download the dataset from Hugging Face and open it as three lazily-loaded “virtual” xarray datasets:

git clone https://github.com/IPL-UV/ClimX.git
cd ClimX
pip install -U "huggingface-hub" xarray netcdf4 dask

from src.data.climx_hf import download_climx_from_hf, open_climx_virtual_datasets

# Download NetCDF artifacts from HF into a local cache directory.
root = download_climx_from_hf("/path/to/hf_cache", variant="full")

# Open as three virtual datasets (lazy / dask-friendly).
ds = open_climx_virtual_datasets(root, variant="full") # or "lite"

ds.hist # historical (targets + forcings)
ds.train # projections training scenarios (targets + forcings; excludes `ssp245` scenario)
ds.test_forcings # `ssp245` scenario forcings only (no targets)

Option 2: download NetCDFs and open with xarray directly

You can also download files from Hugging Face and open them with xarray.

Example:

from huggingface_hub import hf_hub_download
import xarray as xr

path = hf_hub_download(
    repo_id="isp-uv-es/ClimX",
    repo_type="dataset",
    filename="PATH/TO/A/FILE.nc",  # replace with an actual file in this dataset repo
)
ds = xr.open_dataset(path)
print(ds)

Links

• Kaggle competition
• Full dataset (this page)
• Public code repository (challenge materials)
• Website

License and usage

The dataset is released under MIT. In addition, if you are participating in the ClimX competition, please follow the competition rules (notably: restrictions on external climate training data and redistribution of competition data).