Datasets:

isp-uv-es
/

ClimX

	---
	language:
	- en
	pretty_name: "ClimX: extreme-aware climate model emulation"
	tags:
	- climate
	- earth-system-model
	- machine-learning
	- emulation
	- extremes
	- netcdf
	license: mit
	task_categories:
	- time-series-forecasting
	- other
	---

	<!-- NOTE: Math formatting convention: inline math uses \$ ... \$ and math blocks use $$ ... $$. -->

	# 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 288\$ (about \$1^\circ\$) resolution. It also contains the lite-resolution version, with a native grid of \$12 \times 18\$ (about \$16^\circ\$) resolution:

	- Lite-resolution: <1GB, \$16\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:

	$$
	x_t = g(f_t, f_{t-1}, \dots, f_{t-\alpha}, x_{t-1}, x_{t-2}, \dots, x_{t-\beta})
	$$

	where \$f_t\$ are forcings (greenhouse gases + aerosols) and \$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.

	## Evaluation metric

	The primary leaderboard metric is the region-wise normalized Nash–Sutcliffe efficiency (nNSE), averaged over 15 climate extreme indices.

	For each index \$v\$, grid cell \$(i,j)\$, a validity mask \$\mathcal{V}\$ excludes cells with negligible temporal variability. Cell-level \$R^2\$ and nNSE are:

	$$
	R^2_{ij} = 1 - \frac{\mathrm{MSE}_{ij}}{\mathrm{Var}_t(gt_{ij})}, \qquad \mathrm{nNSE}_{ij} = \frac{R^2_{ij}}{2 - R^2_{ij}}
	$$

	For each AR6 land region \$k\$, the area-weighted regional score is:

	$$
	\mathrm{nNSE}_{kv} = \frac{\sum_{(i,j)\in k \cap \mathcal{V}} \cos\phi_i \, \mathrm{nNSE}_{ij}}{\sum_{(i,j)\in k \cap \mathcal{V}} \cos\phi_i}
	$$

	The final score averages uniformly over valid regions and indices:

	$$
	S = \frac{1}{\|V\|} \sum_{v \in V} \frac{1}{\|K_v\|} \sum_{k \in K_v} \mathrm{nNSE}_{kv}
	$$

	\$S=1\$ is perfect agreement, \$S=0\$ corresponds to a mean predictor, and \$S \to -1\$ is pathological.

	## 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:

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

	```python
	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:

	```python
	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](https://www.kaggle.com/competitions/climx)
	- [Full dataset (this page)](https://huggingface.co/datasets/isp-uv-es/ClimX)
	- [Public code repository (challenge materials)](https://github.com/IPL-UV/ClimX)
	- [Website](https://ipl-uv.github.io/ClimX/)

	## 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).