Abstract To achieve the temperature goal of the Paris Agreement, transformative actions are needed. The circular economy (CE) is one concept that gained popularity in recent years, with its proclaimed selling point to combine economic development with benefits to businesses, society, and the environment. However, definitions of CE diverge, applications appear across vastly different settings, and overall there is a lack of understanding of how much CE strategies can contribute to climate change mitigation (mitigation). We systematically screened 3244 records in Web of Science and Scopus, restricted to papers in English. We then selected studies against pre-determined eligibility criteria that, had to (1) refer explicitly to CE or closely related concepts (e.g. performance economy, cradle-to-cradle, material or product efficiency); and (2) refer to a climate change mitigation potential. We identified 341 studies, summarized, and grouped into six sectors (industry, waste, energy, buildings, transport, and agriculture). These sectors are not completely mutually exclusive, but partially overlapping. Nonetheless, sectoral classifications relate to existing categorizations and map well with international assessments of climate change mitigations, such as those of the Intergovernmental Panel on Climate Change (IPCC). Our review sets out to summarize the results of the scientific literature on the extent to which CE strategies can contribute to mitigation. Even though our query explicitly required a consideration of climate change, only 10% of all studies contributed insights on how the CE can support mitigation. We find that the highest saving potential is evidenced in the industry, energy, and transport sector; mid-range savings in the waste and building sector; and lowest gains are to be expected in agriculture. The majority of studies investigate incremental measures claiming but not demonstrating climate change mitigation. Most studies indicate potential but implementation remains weak. Assessments should move from attributional to consequential analysis to avoid misleading policy makers.