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Weather, winds, thermals, and turbulence pose an ever-present challenge to small UAS. These challenges become magnified in rough terrain and especially within urban canyons. As the industry moves towards Beyond Visual Line of Sight (BVLOS) and fully autonomous operations, resilience to weather perturbations will be key. As the human decision-maker is removed from the in-situ environment, producing control systems that are robust will be paramount to the preservation of any Airspace System. Safety requirements and regulations require quantifiable performance metrics to guarantee a safe aerial environment with ever- increasing traffic. In this regards, the effect of wind and weather disturbances on a UAS and its ability to reject these disturbances present some unique concerns. Currently, drone manufacturers and operators rely on outdoor testing during windy days (or in windy locations) and onboard logging to evaluate and improve the flight worthiness, reliability and perturbation rejection capability of their vehicles. Waiting for the desired weather or travelling to a windier location is cost- and time-inefficient. Moreover, the conditions found on outdoor test sites are difficult to quantify and repeatability is non-existent. To address this situation, a novel testing methodology is proposed, combining artificial wind generation thanks to a multi-fan array wind generator (windshaper), coherent GNSS signal generation and accurate tracking of the test subject thanks to motion capture cameras. In this environment, the drone being tested can fly freely, follow missions and experience wind perturbations whilst staying in a modest indoor volume. By coordinating the windshaper, the motion tracking feedback and the position emulated by the GNSS signal generator with the drone’s mission profile, it was demonstrated that outdoor flight conditions can be reliably recreated in a controlled and repeatable environment. Specifically, thanks to real-time update of the position simulated by the GNSS signal generator, it was possible to demonstrate that the drone’s perception of the situation is similar to a corresponding mission being executed outdoor. In this work, the drone was subjected to three distinct flight cases: (1) hover in 2 m s−1 wind, (2) forward flight at 2 m s−1 without wind and (3) forward flight at 2 m s−1 with 2 m s−1 headwind. In each case, it could be demonstrated that by using indoor GNSS signal simulation and wind generation, the drone displays the characteristics of a 20 m move forward, while actually staying stationary in the test volume, within ±1 m. Further development of this methodology opens the door for fully integrated hardware-in- the-loop simulation of drone flight operations.