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.
• 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 evaluated with CRPS.

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_new

Then launch Jupyter and open playground.ipynb:

jupyter notebook

• playground.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: https://www.kaggle.com/competitions/climx
• Hugging Face dataset: https://huggingface.co/datasets/isp-uv-es/ClimX
• Website: https://ipl-uv.github.io/ClimX/