ClimX focuses on emulating high-resolution daily climate outputs from the NorESM2-MM Earth System Model, with special emphasis on accurately reproducing climate extremes (e.g., heatwaves, droughts, and extreme precipitation), not just mean climate behavior.
- Core task: Predict daily, 1° resolution climate variables from greenhouse gas and aerosol forcings.
- Submission rule: Models must predict the daily target variables first; leaderboard indices must be computed from those outputs.
- Data: Full dataset (~200GB, Hugging Face) and lightweight prototype dataset (<1GB, Kaggle).
- Evaluation: Region-wise nNSE averaged across 15 extreme climate indices.
- Test setting: Held-out SSP2-4.5 scenario.
- Optional track: Probabilistic predictions are evaluated with CRPS on a separate Kaggle UQ track.
For this repository, using mamba is recommended for faster and more reliable environment solves.
# one-time: install mamba into base conda
conda install -n base -c conda-forge mamba
# create the environment from this repo
mamba env create -f environment.yml
conda activate clima_emu_newThen launch Jupyter and open playground.ipynb:
jupyter notebookplayground.ipynb: End-to-end notebook for data loading, preprocessing, training baseline models, evaluation, Kaggle submission formatting, and result visualization.environment.yml: Full Python environment specification used by the notebook and training scripts.train.py: Script-based baseline training workflow (useful when you prefer Python scripts over notebooks).src/: Core code for preprocessing, models, metrics, utilities, and evaluation helpers.
- Kaggle main track: https://www.kaggle.com/competitions/climx
- Kaggle UQ track: https://www.kaggle.com/competitions/clim-x-uq-track
- Hugging Face dataset: https://huggingface.co/datasets/isp-uv-es/ClimX
- Website: https://ipl-uv.github.io/ClimX/
ClimX is supported by ESA Phi-lab, which sponsors the challenge prizes and travel support for winning teams.