The latest Assessment Report (AR6) by the Intergovernmental Panel on Climate Change (IPCC) and the Global Stocktake Report under the Paris Agreement emphasize the need for more ambitious climate policies to achieve global CO2 reduction targets. However, a thorough systematic synthesis of the barriers to climate change mitigation policy adoption remains absent. Here, we utilize machine learning to synthesize evidence on barriers to climate change mitigation policy adoption in 11,580 publications across regions and sectors, including energy, transport, and agriculture, forestry, and other land use (AFOLU). We find that distributional dynamic barriers, involving struggles between political coalitions about the appropriate relative burden sharing in climate policy, have become the most prominently researched barrier to climate change mitigation. This challenges dominant collective action theories, which are premised on the importance of free-riding and economic cost barriers. Based on influential studies, we identify four political enablers for overcoming distributional dynamic barriers in a qualitative thematic review that complements our large-scale systematic map: the science-policy interface that can generate the foundation for evidence-based policymaking; participatory governance for trust, shared understanding, and compromise; policy design that promotes positive feedback on actor support through visible policy-induced benefits; and communication and framing to reinforce other enablers, for instance, by raising awareness of benefit-inducing policy designs. More research is needed on viable solutions for overcoming distributional and other climate change mitigation policy adoption barriers, particularly, in the European industry sector, the US transport sector, and the AFOLU sector in Africa and South America. These sectors are underresearched as they are associated with significant emissions yet receive disproportionately little attention in the literature.