Upload README.md

README.md

@@ -0,0 +1,32 @@
+# ClimX: A challenge for extreme-aware climate model emulation
+
+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**.
+
+## What’s in this dataset?
+
+This Hugging Face dataset hosts the ClimX training data as **Zarr** archives, plus a lightweight downscaled variant for prototyping.
+
+- **ClimX.zip**: full dataset (historical + projections)
+- **ClimX-lite.zip**: 16× downscaled “lite” dataset (historical + projections)
+
+The lite dataset is designed to reduce barriers to entry (fast iteration, smaller memory footprint) while keeping the end-to-end workflow consistent.
+
+## Problem summary
+
+Participants train emulators that take **forcing trajectories** (greenhouse gases + aerosols) and optionally past predicted state to produce daily climate fields. The **benchmark target** is not the raw fields themselves, but **15 extreme indices** derived from daily temperature and precipitation.
+
+Primary leaderboard metric (mean standardized MAE over indices):
+
+\[
+S = \frac{1}{15}\sum_{i=1}^{15}\frac{\mathrm{MAE}(\hat{Y}_i, Y_i)}{\sigma_i}
+\]
+
+## Links
+
+- Kaggle competition page: `https://www.kaggle.com/competitions/climx`
+- Public challenge repository: `https://github.com/IPL-UV/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.
+