<p>Radon, a naturally occurring radioactive gas, has garnered significant attention due to its health risks and its potential role as a seismic indicator. Variations in radon levels have been observed in correlation with seismic activity, suggesting that radon could serve as an early warning signal for earthquakes. However, accurately forecasting radon concentrations remains challenging due to the influence of various factors, including meteorological conditions and seasonal fluctuations. Considerable effort has been dedicated to investigating the use of regression models to predict radon levels by incorporating meteorological parameters such as temperature, humidity, and atmospheric pressure. The aim of this study is to improve the modeling of baseline radon concentrations by removing periodic sources of variability (primarily environmental and seasonal effects), rather than directly focusing on radon anomaly detection. Accurate background modeling is a prerequisite for reliable anomaly detection, as it enables a clearer distinction between normal fluctuations and potential anomalies arising from endogenous factors, including seismic or structural phenomena. In particular, we show that the impact of meteorological parameters on radon prediction can be effectively replaced by a seasonal regression model based on temporal dummy variables, without significant loss in predictive accuracy. This method offers a promising alternative for radon modeling, enabling early estimation of average radon levels and facilitating the timely identification of anomalous behavior. Our findings suggest that seasonal regression models based on dummy variables provide a robust and accurate framework for forecasting radon, with potential implications for improved seismic monitoring. Importantly, any future application to anomaly detection must also account for structural changes at the measurement site, which may independently affect radon emissions.</p>