Hemorrhagic fever with renal syndrome (HFRS) is a climate-sensitive infectious disease. The effect of climatic drivers might predict and prevent HFRS, and understanding their relationship is urgently needed in the face of climate change. This study aimed to investigate the effect of meteorological factors on HFRS incidence. The random forest regression model, generalized additive model, and distributed lag nonlinear model (DLNM) were constructed to predict the importance, nonlinear trend and interaction effect, and exposure-lag effect of meteorological factors on HFRS incidence based on the data obtained in Shandong Province, China, 2013-2022. The most crucial meteorological factor was the weekly mean temperature. Interaction results showed that relative humidity affected HFRS incidence only under high or low-temperature conditions, and the effect of relative humidity with high and low pressure was the opposite. Using the median value as the reference, DLNM indicated that extremely low temperature had significant associations with HFRS at a lag of 3-5 weeks. Under extremely high temperatures, relative risks (RRs) became significantly high from a lag of 11 weeks, with the lowest value of 1.07 (95% CI: 1.00-1.13). RRs increased and then decreased with increasing mean temperature at lag 4 and 8 weeks, whereas at lag 12 and 16 weeks, the RRs gradually increased as the mean temperature climbed. This study demonstrates the complex relationship between meteorological factors and HFRS incidence. Our findings provide implications for the development of weather-based HFRS early warning systems.