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Objectives: This article aims to quantify the relationship between climate variations and Hemorrhagic fever with renal syndrome (HFRS) in Weifang, a city in Shandong Province, eastern China with a serious HFRS epidemic, to reach a better understanding of the effect of climate variations on HFRS.Methods: The incidence of HFRS and climate variables during the period 2004-2012 has been studied on a monthly basis.Cross correlation between each climate variable and incidence of HFRS was conducted in consideration of lag effects.Seasonal autoregressive integrated moving average (SARIMA) models were used to perform the regression analysis.Results: Monthly mean maximum temperature, minimum temperature, relative humidity, and accumulative precipitation were all significantly correlated with the incidence of HFRS with different lag effects in WeiFang.After controlling for the seasonality, lag time, and long-term trend, the SARIMA model suggested that monthly mean temperature (β =-0.196, P=0.024) and monthly mean minimum temperature (β =0.194,P=0.033) were correlated with the monthly incidence of HFRS.Conclusions: Climate variations affect the transmission of HFRS in China.Monthly mean maximum temperature and monthly mean minimum temperature could be used as predictors of the occurrence of HFRS.Public health interventions in HFRS should be undertaken in consideration of the impact of climate change in the future.