In this work we present theoretical analyses and experimental evidence on properties and limitations of meteorological normalization. Recognizing that meteorological normalization is heavily influenced by the dependency of meteorological descriptors on seasonality, our discussion emphasizes the need for future work to explore how to cope with these limitations. Following the theoretical analysis presented in this work, one avenue to solving this is de-seasoning meteorological data to guarantee their independence. While in linear models this de-seasoning is related to effect estimation, such an endeavour becomes rather com-275 plicated with non-linear models where effect estimation is non-trivial. Excluding the temporal component of meteorological variables at a higher level in nonlinear regression models may help mitigating issues and improve the overall validity of using meteorological normalization. An alternative approach to making the method more reliable is considering the distributions of the data and hence avoiding impossible combinations of meteorological and non-meteorological variables (such as simultaneous snow, rain, and sun in summer) during resampling. Future work shall be devoted to determine the success and applicability 280 of these approaches for the prediction of air pollutant concentrations.