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Recent years have seen the introduction of large- scale platforms for experimental wireless research. These platforms, which include testbeds like those of the PAWR program and emulators like Colosseum, allow researchers to prototype and test their solutions in a sound yet realistic wireless environment before actual deployment. Emulators, in particular, enable wire- less experiments that are not site-specific as those on real testbeds. Researchers can choose among different radio frequency (RF) scenarios for real-time emulation of a vast variety of different situations, with different numbers of users, RF bandwidth, antenna counts, hardware requirements, etc. Although very powerful, in that they can emulate virtually any real-world deployment, emulated scenarios are only as useful as how accurately they can capture the targeted wireless channel and environment. Achieving emulation accuracy is particularly challenging, especially for experiments at scale for which emulators require considerable amounts of computational resources. In this paper we propose a framework to create RF scenarios for emulators like Colosseum from rich forms of inputs, like those obtained by measurements through radio equipment or via software (e.g., ray-tracers and electromagnetic field solvers). Our framework optimally scales down the large set of RF data in input to the fewer parameters allowed by the emulator by using efficient clustering techniques and channel impulse response re-sampling. We showcase our method by generating wireless scenarios for Colosseum by using Remcom’s Wireless InSite, a commercial-grade ray-tracer that produces key characteristics of the wireless channel. Examples are provided for line-of-sight and non-line-of-sight scenarios on portions of the Northeastern University main campus.