Upload README.md

README.md

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-# ClimX: A challenge for extreme-aware climate model emulation
+---
+language:
+  - en
+pretty_name: "ClimX: extreme-aware climate model emulation"
+tags:
+  - climate
+  - earth-system-model
+  - machine-learning
+  - emulation
+  - extremes
+  - netcdf
+license: mit
+task_categories:
+  - regression
+  - time-series-forecasting
+---
 
-ClimX is a competition focused on developing **fast and accurate machine learning emulators** for the NorESM2-MM Earth System Model, with evaluation centered on **climate extremes**.
+<!-- NOTE: Math formatting convention: inline math uses \\( ... \\) and math blocks use $$ ... $$. -->
 
-## What’s in this dataset?
+# ClimX: a challenge for extreme-aware climate model emulation
 
-This Hugging Face dataset hosts the ClimX **full-resolution training data** (historical + projections). A lightweight, \\(16\times\\) spatially coarsened variant is provided separately for rapid prototyping (hosted on Kaggle).
+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.
 
-The full dataset is distributed in **NetCDF-4** format to support broad compatibility with common climate tooling.
+## Dataset summary
 
-## Problem 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:
 
-Participants train emulators that take **forcing trajectories** (greenhouse gases + aerosols) and optionally past predicted state to produce daily climate fields at the native NorESM2-MM grid (\\(192 \times 288\\)). The **benchmark target** is not the raw fields themselves, but **15 extreme indices** derived from daily temperature and precipitation.
+- **Lite-resolution**: <1GB, **\\(16\times\\)** spatially coarsened, meant for rapid prototyping.
+- **Full-resolution**: ~200GB, full-resolution data for large-scale training.
 
-Primary leaderboard metric (mean standardized MAE over indices):
+## 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:
 
 $$
-S = \frac{1}{15}\sum_{i=1}^{15}\frac{\mathrm{MAE}(\hat{Y}_i, Y_i)}{\sigma_i}
+x_t = g(f_t, f_{t-1}, \dots, f_{t-\alpha}, x_{t-1}, x_{t-2}, \dots, x_{t-\beta})
 $$
 
-Here \\(\sigma_i\\) is computed from the ground-truth \\(Y_i\\) values for the evaluation split (public/private) and held fixed for all submissions on that split.
+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.
+
+## 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
 
-This dataset is provided for the ClimX competition and associated research/education use. Please follow the competition rules regarding external data/model restrictions and data redistribution.
+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).
+
+## Citation
+
+If you use this dataset in academic work, please cite the ClimX challenge materials and the underlying NorESM2 model description:
+
+- Seland, Ø., Bentsen, M., Graff, L. S., et al. (2020). *The Norwegian Earth System Model, NorESM2 – Evaluation of the CMIP6 DECK and historical simulations*. Geoscientific Model Development.