OBJECTIVES: To examine the nonlinear effects of meteorological factors on H7N9 incidence and to determine what meteorological measure and on which day preceding symptom onset has the most significant effect on H7N9 infection. METHODS: We applied a zero truncated Poisson regression model incorporating smoothed spline functions to assess the nonlinear effect of temperature (maximum, minimum and daily difference) and relative humidity on H7N9 human case number occurred in China from February 19, 2013 to February 18, 2014, adjusting for the effect of age and gender. RESULTS: Daily minimum or maximum temperature significantly contributed to the human infection with H7N9 virus. The models incorporating the nonlinear effect of minimum or maximum temperature on day 13 prior to disease onset have the best predictive ability. For minimum temperature, the high risk ranges about 5 degrees C to 9 degrees C; the moderate risk ranges about -10 degrees C to 0 degrees C and the low risk is above 9 degrees C. For maximum temperature, the high risk ranges about 13 degrees C to 18 degrees C, the moderate risk ranges about 0 degrees C to 4 degrees C and low risk is above 18 degrees C. The relative humidity was not significantly associated with the incidence of infection. The incidence of H7N9 was higher for males compared with females (p-value<0.01) and it peaked around 60-70 years old. CONCLUSIONS: We provide direct evidence to support the role of climate conditions in the spread of H7N9 and thereby address a critical question fundamental to the understanding of the H7N9 epidemiology and evolution. These findings could be used to inform targeted surveillance and control efforts in reducing the future spread of H7N9.