Upload README.md

README.md

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+---
+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).