A new version of the p-TOMCAT Chemical Transport Model (CTM) which includes an improved photolysis code, Fast-JX, is validated. Through offline testing we show that Fast-JX captures observed J(NO2) and J(O1D) values well, though with some overestimation of J(O1D) when comparing to data retrieved during a flight. By comparing p-TOMCAT output of CO and ozone with measurements, we find that the inclusion of Fast-JX in the CTM strongly improves the latter's ability to capture the seasonality and levels of tracers' concentrations. A probability distribution analysis demonstrates that photolysis rates and oxidant (OH, ozone) concentrations cover a broader range of values when using Fast-JX instead of the standard two-stream photolysis code. This is not only driven by improvements in the seasonality of cloudiness but also even more by the better representation of cloud spatial variability. We use three different cloud treatments to study the radiative effect of clouds on the abundances of a range of tracers and find only modest effects on a global scale. This is consistent with the most relevant recent study. The new version of the validated CTM will be used for a variety of future studies examining the variability of tropospheric composition and its drivers.