 Given these challenges, climate model evaluation today often focuses primarily on describing the extent to which modeling results fit with observations and on characterizing how that fit has improved from one generation of models to the next; it tends to reach only relatively modest conclusions about the adequacy of climate models for particular purposes. For example, recent Intergovernmental Panel on Climate Change (IPCC) reports have concluded that today’s climate models provide “credible quantitative estimates” of climate system conditions in support of climate change detection, attribution and projection (see  Section 5),  where this credibility is underwritten both by how the models are constructed—i.e., their grounding in basic physical principles—and by how they perform on a wide range of metrics (see Randall et al. 2007; Flato et al. 2013; see also Knutti 2008). The credibility of results is judged to be greater for larger spatial scales (i.e., for quantities averaged over global or continental scales) and for variables that are less spatially heterogeneous (e.g., for temperature and pressure as opposed to precipitation), but the expected accuracy of modeling results used in detection, attribution and projection studies is rarely explicitly quantified. To begin to move the discussion forward, Baumberger et al. (2017) draw on both philosophical and scientific resources to outline a conceptual framework intended to aid evaluation of the adequacy of climate models for particular purposes; they point to empirical accuracy, robustness, and coherence with background knowledge as relevant considerations (see also Knutti (2018: 11.4), highlighting the importance of "process understanding").