Climate has critical roles in the origin, pathogenesis and transmission of infectious zoonotic diseases. However, large-scale epidemiologic trend and specific response pattern of zoonotic diseases under future climate scenarios are poorly understood. Here, we projected the distribution shifts of transmission risks of main zoonotic diseases under climate change in China. First, we shaped the global habitat distribution of main host animals for three representative zoonotic diseases (2, 6, and 12 hosts for dengue, hemorrhagic fever, and plague, respectively) with 253,049 occurrence records using maximum entropy (Maxent) modeling. Meanwhile, we predicted the risk distribution of the above three diseases with 197,098 disease incidence records from 2004 to 2017 in China using an integrated Maxent modeling approach. The comparative analysis showed that there exist highly coincident niche distributions between habitat distribution of hosts and risk distribution of diseases, indicating that the integrated Maxent modeling is accurate and effective for predicting the potential risk of zoonotic diseases. On this basis, we further projected the current and future transmission risks of 11 main zoonotic diseases under four representative concentration pathways (RCPs) (RCP2.6, RCP4.5, RCP6.0, and RCP8.5) in 2050 and 2070 in China using the above integrated Maxent modeling with 1,001,416 disease incidence records. We found that Central China, Southeast China, and South China are concentrated regions with high transmission risks for main zoonotic diseases. More specifically, zoonotic diseases had diverse shift patterns of transmission risks including increase, decrease, and unstable. Further correlation analysis indicated that these patterns of shifts were highly correlated with global warming and precipitation increase. Our results revealed how specific zoonotic diseases respond in a changing climate, thereby calling for effective administration and prevention strategies. Furthermore, these results will shed light on guiding future epidemiologic prediction of emerging infectious diseases under global climate change.