Abstract: Shenzhen is very unique in population structure as well as social and economic developments, but the relationship between its transitional monsoon climate from the tropics to subtropics and the influenza morbidity has rarely been evaluated. In order to reveal the epidemiologic features of influenza in Shenzhen, long-term surveillance data of influenza like illness (ILI) for the recent 17 years (2003—2019) are collected. The distributed-lag nonlinear model (DLNM) is adopted to investigate the associations between ILI morbidity and climatic variables. Further, the Prophet time-series approach and a stepwise-regression model are used to predict influenza risks, respectively. The results reveal that the number of ILI outpatients increases at the early stage of the study period (2003—2009), then stabilizes between 2010 and 2014, and decreases slightly from 2015 to 2019. Besides, a relatively clear annual cycle of ILI morbidity is discovered, which shows a unimodal pattern in most of the years. The number of ILI cases usually peaks during the summer months, which is highly consistent with the hot and humid climatic background. A second peak that is usually related to large-scaled influenza epidemics occurs occasionally at the end of the year. The DLNM shows that high air temperature has substantial and immediate impact on the ILI risk (T=29.9℃, RR=1.237, 95%CI: 1.203—1.272), while the effect of low temperature is relatively weak and occurs with a significant lag (2—3 weeks). Relative humidity of 70%—75% can induce the highest risk of ILI (e.g., RH=70%, RR=1.089, 95%CI: 1.046—1.135), and it also has synergistic cross-effect with high temperature. Besides, the number of ILI outpatients increases prominently when daily temperature range (DTR) is within in 4—6℃ or >9℃, indicating a nonlinear relation between DTR and the spread of influenza virus. Due to the overall low wind speed in Shenzhen, its effect on ILI is insignificant. The correct rates of prediction by the Prophet time-series approach and multiple-linear regression model are similar to each other (>86%). Nevertheless, the prediction accuracy of the regression model is relatively high (>80%) when considering meteorological factors and previous ILI cases simultaneously. In summary, the ILI morbidity in Shenzhen is significantly and nonlinearly associated with the synergistic effect of air temperature and humidity. Thus, the influenza risk is to some extent predictable.