The advancement of Advanced Driver Assistance System (ADAS) technologies offers tremendous benefits. ADAS features such as emergency braking, blind-spot monitoring, lane departure warning, adaptive cruise control, etc., are promising to lower on-road accident rates and severity. With a common goal for the automotive industry to achieve higher levels of autonomy, maintaining ADAS sensor performance and reliability is the core to ensuring adequate ADAS functionality. Currently, the challenges faced by ADAS sensors include performance degradation in adverse weather conditions and a lack of controlled evaluation methods. Outdoor testing encounters repeatability issues, while indoor testing with a stationary vehicle lacks realistic conditions. This study proposes a hybrid method to combine the advantages of both outdoor and indoor testing approaches in a Drive-thru Climate Tunnel (DCT). The proposed DCT features a test section that is isolated from the surrounding environment and allows a vehicle to move through a volume of precisely simulated precipitation. It is constructed as a model scale prototype for concept demonstration and preliminary studies. In addition, the DCT’s modular design allows for varying distances, vehicle speeds, and precipitation rates during testing. The model vehicle is equipped with common ADAS sensors, such as optical cameras and LiDARs, which are known to be heavily affected by adverse weather. Quantification metrics are designed and applied to ADAS datasets to investigate sensor performance in conjunction with related phenomena, such as the perceived rain characteristics of a moving vehicle. Therefore, the DCT provides a platform to bridge the gap between outdoor and indoor weather testing for ADAS sensors and open opportunities for sensor perception developments.