The largest source of energy consumption in build-ings is heating, ventilation, and air conditioning (HVAC). For an HVAC system to provide comfort and minimize energy consumption, it is crucial to understand the spatio-temporal thermal dynamics, especially in large open spaces. To optimize HVAC control, it is important to establish accurate dynamic thermal models. For this purpose, we constructed a real-world testbed by instrumenting an HVAC-controlled auditorium using multiple types of sensors. Based on the dataset, we develop and evaluate a novel data-driven approach to model the complex thermal dynamics in a large space through a combination of data clustering and system identification techniques. Real-world data shows that our approach achieves low estimation errors. Our modeling approach therefore provides a practical foundation for HVAC control and optimization for large open spaces.