ClimateLearn is a Python library for accessing state-of-the-art climate data and machine learning models in a standardized, straightforward way. This library provides access to multiple datasets, a zoo of baseline approaches, and a suite of metrics and visualizations for large-scale benchmarking of statistical downscaling and temporal forecasting methods.

Please refer to this Google Colab.

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conda is required. We recommend installing miniconda.

First, create a conda environment and install the conda-only dependencies.

$ conda create -n cl_env xesmf==0.7.0 -c conda-forge -y

Then, install the rest of the library with pip.

$ conda activate cl_env
$ pip install -e .

Alternatively, these two steps can be combined into a single command.

$ conda env create -n cl_env -f binder/environment.yml

• Weights & Biases

ClimateLearn is managed by the Machine Intelligence Group at UCLA, headed by Professor Aditya Grover.

If you use ClimateLearn, please see the CITATION.cff file or use the citation prompt provided by GitHub in the sidebar.