Machine learning is emerging as a valuable tool in mitigating and adapting to climate change, while climate change has been noted as a valuable area for inspiring cutting-edge algorithms in machine learning. This workshop is intended to form connections and foster cross-pollination between researchers in machine learning and experts in complementary climate-relevant fields, in addition to providing a forum for those in the machine learning community who wish to tackle climate change. This workshop distinguishes itself from previous editions of the popular ‘Tackling Climate Change with Machine Learning’ workshop series by focusing on a key challenge: questioning common machine learning assumptions in the context of climate impact. Specifically, we will concentrate on two questions that are very timely for the machine learning community: (i) the various climate-related benefits and costs of large vs small models, (ii) the design of effective benchmarks for climate-related applications.