Climate change adaptation involves the management of climate-related risks, and the IPCC says we must prioritize adaptation immediately. However, researchers and policymakers have little systematic understanding of which adaptations are effective at reducing risks, including under different climate conditions. Drawing on data from human communities past and present, we review how features of climate variability—temporal autocorrelation, frequency, and severity—may predict which candidate climate change adaptations communities innovate or adopt. Using a case study of climate and remittances in Africa, we outline how researchers can characterize features of climate data relevant to adaptation—autocorrelation, frequency, and severity—and then qualitatively compare these data to candidate adaptations. We include suggestions for how to involve communities in these explorations, from setting climate thresholds to identifying impactful hazards. By better understanding the relationship between climate variability and common solutions used by communities, researchers and policymakers can better support communities as they adapt to contemporary climate change.