README.md

metadata

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

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.

Evaluation metric

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

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

$$R_{ij}^2=1-\frac{{\mathrm{M}\mathrm{S}\mathrm{E}}_{ij}}{{\mathrm{V}\mathrm{a}\mathrm{r}}_t(g{t}_{ij})},{\mathrm{n}\mathrm{N}\mathrm{S}\mathrm{E}}_{ij}=\frac{R_{ij}^2}{2-R_{ij}^2}R^2\_\{ij\}\; =\; 1\; -\; \backslash frac\{\backslash mathrm\{MSE\}\_\{ij\}\}\{\backslash mathrm\{Var\}\_t(gt\_\{ij\})\},\; \backslash qquad\; \backslash mathrm\{nNSE\}\_\{ij\}\; =\; \backslash frac\{R^2\_\{ij\}\}\{2\; -\; R^2\_\{ij\}\}$$

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

$${\mathrm{n}\mathrm{N}\mathrm{S}\mathrm{E}}_{kv}=\frac{\sum _{(i,j)\in k\cap \mathcal{V}}\cos {\varphi }_i{\mathrm{n}\mathrm{N}\mathrm{S}\mathrm{E}}_{ij}}{\sum _{(i,j)\in k\cap \mathcal{V}}\cos {\varphi }_i}\backslash mathrm\{nNSE\}\_\{kv\}\; =\; \backslash frac\{\backslash sum\_\{(i,j)\backslash in\; k\; \backslash cap\; \backslash mathcal\{V\}\}\; \backslash cos\backslash phi\_i\; \backslash ,\; \backslash mathrm\{nNSE\}\_\{ij\}\}\{\backslash sum\_\{(i,j)\backslash in\; k\; \backslash cap\; \backslash mathcal\{V\}\}\; \backslash cos\backslash phi\_i\}$$

The final score averages uniformly over valid regions and indices:

$$S=\frac{1}{\mathrm{\mid }V\mathrm{\mid }}\sum _{v\in V}\frac{1}{\mathrm{\mid }{K}_v\mathrm{\mid }}\sum _{k\in K_v}{\mathrm{n}\mathrm{N}\mathrm{S}\mathrm{E}}_{kv}S\; =\; \backslash frac\{1\}\{|V|\}\; \backslash sum\_\{v\; \backslash in\; V\}\; \backslash frac\{1\}\{|K\_v|\}\; \backslash sum\_\{k\; \backslash in\; K\_v\}\; \backslash mathrm\{nNSE\}\_\{kv\}$$ $S=1S=1$ is perfect agreement, $S=0S=0$ corresponds to a mean predictor, and $S\to -1S\; \backslash to\; -1$ 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:

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).