As an automated verification and validation tool, model checking can be quite effective in practice. Nevertheless, model checking has been quite inefficient when dealing with systems with data variables over a large (or infinite) domain, which is a serious limiting factor for its applicability in practice.To address this issue, we have investigated a static abstraction technique, domain reduction abstraction, based on data equivalence and trajectory reduction, and implemented it as a prototype extension of the symbolic model checker NuSMV. Unlike on-the-fly dynamic abstraction techniques, domain reduction abstraction statically analyzes specifications and automatically produces an abstract model which can be reused over time-a feature suitable for regression verification.