  Climate models have been key for assessing the impact of climate change and
simulating future climate scenarios. The machine learning (ML) community has
taken an increased interest in supporting climate scientists' efforts on
various tasks such as climate model emulation, downscaling, and prediction
tasks. Many of those tasks have been addressed on datasets created with single
climate models. However, both the climate science and ML communities have
suggested that to address those tasks at scale, we need large, consistent, and
ML-ready climate model datasets. Here, we introduce ClimateSet, a dataset
containing the inputs and outputs of 36 climate models from the Input4MIPs and
CMIP6 archives. In addition, we provide a modular dataset pipeline for
retrieving and preprocessing additional climate models and scenarios. We
showcase the potential of our dataset by using it as a benchmark for ML-based
climate model emulation. We gain new insights about the performance and
generalization capabilities of the different ML models by analyzing their
performance across different climate models. Furthermore, the dataset can be
used to train an ML emulator on several climate models instead of just one.
Such a "super emulator" can quickly project new climate change scenarios,
complementing existing scenarios already provided to policymakers. We believe
ClimateSet will create the basis needed for the ML community to tackle
climate-related tasks at scale.
