A number of market factors such as customer demand for improved connectivity and infotainment systems, automated driver assist systems and electrification of powertrain have driven an increase in the number of electrical systems and components within the cabin of automotive vehicles. These systems have limited operating temperature windows, therefore markets with high ambient temperatures and solar loading represent a significant challenge for these systems due to high cabin temperatures. Traditionally climatic facilities have been used replicate the conditions seen in these markets in order to understand the performance of these systems. However such facilities have a number of limitations such as fixed solar arrays, secondary radiation from the walls and substantial operating costs limiting testing to envelope tests. Therefore the requirement for CAE based approach to more accurately represent the conditions seen in the real world is clear. To this end this work presents a CAE method for predicting component and ambient temperatures within the cabin. Prior works have focused on modelling the A-surface and air temperature distribution within the cabin in order to predict HVAC performance characteristics. However in order to accurately predict the temperatures of the electrical systems the model must be capable of calculating the heat transfer through the components within the cabin; thus a higher level of fidelity is required for both the geometry and material properties. Unlike many previous works the modelling strategy utilises a standalone thermal solver without the need for a coupled CFD solver and thus is able to greatly reduce the computational resource requirements, whilst maintaining an acceptable level of accuracy. To improve the understanding of the effect environmental factors have on cabin temperatures as well as ensure accuracy computational results an experimental methodology has been developed to collect in-field data. The key features of the test procedure include comprehensive instrumentation of vehicle cabin to measure ambient and surface temperatures, characterisation of the ambient conditions local to the vehicle including; direct, diffuse and global solar irradiance, temperature, wind speed and direction. The results generated by the computational model have been correlated against those collected from the climatic wind tunnel and in-field to ensure robust behaviour, including a detailed comparison of the temperature distributions for the two dataset to determine the validity of wind tunnel testing.