Metal oxides (MOx) represent one of the most investigated chemiresistive gas sensing platforms in spite of the challenges in selectivity to analytes and interference from humidity (RH). While se- lectivity is traditionally improved by cross-referencing sensor arrays, interferences from humidity (RH) in ambient environment, to which the majority of the MOx materials are susceptible, can- not be inherently quantified. For standalone MOx sensors, it is therefore difficult to discriminate responses from analytes and humidity. We develop a strategy which employs temperature modu- lation (TM) algorithms and machine learning (ML) approaches using principal component analy- sis (PCA) and cluster analysis of transient features, to quantify NO2 concentrations under specific RH conditions. With a single inkjet-printed MOx/reduced graphene oxide (rGO) complementary metal-oxide-semiconductor (CMOS)-integrated sensor, we achieve an overall discrimination ac- curacy of 97.3%. Our approach may enable the development of predictive systems for humidity sensitive sensors under ambient moisture conditions, towards the realisation of low-power, minia- turised adaptive air quality monitoring.