Automated control in Cyber-Physical Systems (CPS) generates behaviours that may surprise non-expert users. Relevant explanations are required to maintain user trust. Large CPS (e.g., autonomous car networks and smart grids) raise additional scaleability issues for the explanatory processes and complexity issues for generated explanations. We propose a multi-scale system modelling and explanation technique to address these concerns. The idea is to increase the scale, or abstraction level, of the modelled CPS, whenever possible without loss of salient information, so as to produce smaller system representations and hence to reduce the complexity of the explanatory process and of the generated explanations. We illustrate our proposal via an urban traffic case study, modelling traffic at two different scales (i.e., modelling individual cars at a lower-scale; and traffic jams at a higher-scale). We show how a multi-scale explanatory process can use the lower- and higher-scale models to generate either longer (more detailed) explanations, or shorter (more abstract) explanations, respectively. This proof-of-concept illustration offers a basis for further research towards a comprehensive multi-scale explanatory solution for CPS.