ClimX: A Benchmark for Extreme-Aware Climate Model Emulation

_{Prizes sponsored by ESA Φ-lab}

ClimX is a persistent benchmark for 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. A September 1–December 15, 2026 Kaggle challenge, with €3,300 in prizes sponsored by ESA Φ-lab, serves as a launch event to seed community use and establish an initial leaderboard.

At a glance

• 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.

Getting started (recommended)

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 and key files

• 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.
• rnNSE.py: Exact reference implementation of the Kaggle main-track leaderboard metric.

Main-track metric

The main track uses region-wise normalized Nash–Sutcliffe efficiency (nNSE). For each climate index, a grid cell is valid when the population variance of its ground-truth time series is greater than 1e-6 (ddof=0). Only cells in the embedded AR6 land-region raster are scored. Cell nNSE values are weighted by cosine latitude within each region; valid regions are then weighted equally, followed by equal weighting across indices.

The scorer expects complete 192×288 maps with rows ordered as time, latitude, then longitude. NaN and infinite predictions are invalid. See rnNSE.py for the exact implementation used by the competition.

Benchmark and challenge links

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

Prizes and sponsorship

The launch challenge runs from September 1 through December 15, 2026; the benchmark remains available afterward.

Main-track prizes totalling €3,300 are sponsored by ESA Φ-lab (European Space Agency):

• 1st place: €1,800
• 2nd place: €1,000
• 3rd place: €500

Prizes go to the top three main-track teams on the official leaderboard metric, subject to open-sourcing code and weights and passing finalist reproducibility checks.

Publication

The challenge results will be submitted as a competition paper to a leading peer-reviewed journal in the field. The top three teams will be invited to contribute as co-authors, subject to meaningful scientific contributions and the journal's authorship requirements.