HVAC energy savings have been a key focus of recent building energy management efforts, but the savings typically reduce thermal comfort for building occupants. Established thermal comfort models use complex and person-specific parameters, such as clothing insulation and metabolic rate, to predict individual comfort levels, making the design of automated comfort modelling systems a highly challenging endeavour. In this paper, we investigate the use of humidex, which encapsulates both temperature and humidity, as an easily measurable and highly representative indoor thermal comfort predictor. We verify the feasibility of humidex as an indoor thermal comfort predictor by contrasting its performance to that of the best feature set (the feature set that best predicts the thermal comfort) constructed jointly by recursive sequential forward selection and support vector regression. The analysis using the global datasets, including data from seven climate zones across three continents, shows that humidex is a favourable and easily measurable indoor thermal comfort descriptor when humidity is significant. We take away the message from the analysis and design, develop, and deploy a system that couples a deployment of sensor nodes in an office environment, where each node collects the ambient temperature and humidity at each person's desk, and an automated survey mechanism to record people's thermal sensation votes. We use subjective response through the surveys as ground truth to validate the performance of the thermal comfort prediction using humidex. The results confirm our analysis of global datasets and show that humidex is a good predictor of indoor thermal comfort at high humidity.