The purpose of our study was to establish a seasonal model to simulate the oscillation of the number of influenza cases with weather conditions and calendar months in Tokyo, Japan, during the winter season. Surveillance data for influenza in Tokyo was retrieved from the Infectious Agents Surveillance Report, published by the National Institute of Infectious Diseases in Japan. We obtained data for 86 parameters of weather conditions from the Meteorological Agency. The best-fit model was built by multiple regression with stepwise analysis. The reported number of patients with influenza per week was significantly increased with fewer days of maximum temperature 10 per week (T10) and more days of relative humidity <60% per week (S60), adjusted by calendar month, average temperature, and vapor pressure. Annual oscillation of the number of reported influenza cases at the start, peak, and end weeks almost exactly matched the model, although peak levels for each oscillation did not always match. However, this model showed that 81% of the variation among the observed number of influenza cases was explained by a linear relationship with the seasonal parameters utilized. The validity of this model applied to data from 1999 to 2002, showing a 75% correlation. Using this model, if the number of days with both T10 and S60 increased by one per week, the number of influenza cases was simulated to decrease by approximately half. These results suggest that most of the oscillation in the number of influenza cases may be explained using a seasonal model that can simulate the impact of global warming.