Impact attribution covers a diverse set of qualitative and quantitative approaches, building on experimental approaches, observations from remote sensing, long-term in situ observations and monitoring efforts, teamed with LK, process understanding and empirical or dynamical modelling . The attribution of a change in a natural or human system  to changes in climate-related systems  requires accounting for other potential drivers of change, such as technological and economic changes in agriculture affecting crop production , changes in human population patterns and vulnerability affecting flood or wildfire induced damages , or habitat loss driving declines in wild species . These drivers are accounted for by estimating a baseline condition that would exist in the absence of climate change. The baseline might be stationary and be approximated by observations from the past, or it may change over time and be simulated by statistical or process-based impact models . Assessment of multiple independent lines of evidence, taken together, can provide rigorous attribution when more quantitative approaches are not available . These include palaeodata, physiological and ecological experiments, natural 'experiments' from very long-term datasets indicating consistent responses to the same climate trend/event and 'fingerprints' in species' responses that are uniquely expected from climate change . Meta-analyses of species/ecosystem responses, when conducted with wide geographic coverage, also provide a globally coherent signal of climate change at an appropriate scale for attribution to anthropogenic climate change .
IPCC
Climate Change 2022: Impacts, Adaptation and Vulnerability
IPCC Report