# ThousandWorlds: A benchmark for climate emulation of potentially habitable exoplanets

arXiv: https://arxiv.org/abs/2606.18338 · HTML: https://mlsanity.com/paper/2606.18338/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 23 of its 96 references that are in the map.
- First posted on arXiv 16 Jun 2026.

## Builds on (most cited first, 23 of 23)

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- [ClimSim-Online: A Large Multi-scale Dataset and Framework for Hybrid ML-physics Climate Emulation](https://mlsanity.com/paper/2306.08754/) (2023)
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- [Spatiotemporal Pyramid Flow Matching for Climate Emulation](https://mlsanity.com/paper/2512.02268/) (2025)
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- [LFRic: Meeting the challenges of scalability and performance portability in Weather and Climate models](https://mlsanity.com/paper/1809.07267/) (2018)
- [Performance and scaling of the LFRic weather and climate model on different generations of HPE Cray EX supercomputers](https://mlsanity.com/paper/2409.15859/) (2024)
- [Accelerating exoplanet climate modelling: A machine learning approach to complement 3D GCM grid simulations](https://mlsanity.com/paper/2508.10827/) (2025)
- [Accelerating Radiative Transfer for Planetary Atmospheres by Orders of Magnitude with a Transformer-Based Machine Learning Model](https://mlsanity.com/paper/2510.27050/) (2025)
- [Gaussian Process Latent Factor Regression for Low-Data, High-Dimensional Output Problems](https://mlsanity.com/paper/2606.06576/) (2026)

## Abstract (arXiv)

The search for life beyond Earth will depend on detecting faint signatures in the atmospheres of potentially habitable exoplanets. Interpreting those signatures requires understanding the host planet's climate: the same molecule may signal life on one planet and abiotic chemistry on another. Global climate models (GCMs) provide this understanding, but individual runs can require up to millions of core-hours and substantial domain expert time. Machine-learning emulators could remove this bottleneck, but progress has been limited by the absence of a curated, multi-model exoclimate dataset. We introduce ThousandWorlds, an ML-ready benchmark for exoclimate emulation and for the broader regime of low-data, multi-simulator, parameter-to-field regression. The dataset contains approximately 1,700 simulations from five GCMs, mapping eight planet parameters to 3D atmospheric fields including temperature, humidity, winds, clouds, and radiation. Three nested subsets define progressively harder challenges: single-simulator regression, multi-simulator regression with complete observations, and multi-simulator regression with structured missingness. We propose two evaluation protocols: one for ranking methods, and one that measures performance relative to the disagreement between GCMs themselves. We evaluate ten baselines spanning simple methods, trees, deep learning, and Gaussian processes. GP-based methods perform best, suggesting that ThousandWorlds exposes a regime where off-the-shelf deep learning does not yet succeed. Data: https://doi.org/10.57967/hf/8695. Code: https://github.com/edstevenson/ThousandWorlds.