Over the past two decades, Cyber-Physical Systems (CPS) have emerged as critical components in various industries, integrating digital and physical elements to improve efficiency and automation, from smart manufacturing and autonomous vehicles to advanced healthcare devices. However, the increasing complexity of CPS and their deployment in highly dynamic contexts undermine user trust. This motivates the investigation of methods capable of generating explanations about the behavior of CPS. To this end, Explainable Artificial Intelligence (XAI) methodologies show potential. However, these approaches do not consider contextual variables that a CPS may be subjected to (e.g., temperature, humidity), and the provided explanations are typically not actionable. In this article, we propose an Actionable Contextual Explanation System (ACES) that considers such contextual influences. Based on a user query about a behavioral attribute of a CPS (for example, vibrations and speed), ACES creates contextual explanations for the behavior of such a CPS considering its context. To generate contextual explanations, ACES uses a context model to discover sensors and actuators in the physical environment of a CPS and obtains time-series data from these devices. It then cross-correlates these time-series logs with the user-specified behavioral attribute of the CPS. Finally, ACES employs a counterfactual explanation method and takes user feedback to identify causal relationships between the contextual variables and the behavior of the CPS. We demonstrate our approach with a synthetic use case; the favorable results obtained, motivate the future deployment of ACES in real-world scenarios.