The impact of climate change on food security is attracting ever increasing attention from governments and academics.However,the previous studies on assessing the effects of climate change on grain yield-which used different impact factors and methodshave led to different,even contrasting results.This situation confuses agricultural and climate change economists when choosing the yield loss for assessing the impact of climate change on food security.This paper aims to provide the impact of temperature and precipitation changes on the yields of Chinas staple crops under a unified scenario by using the results of the existing research on assessment of the impact of climate change on Chinas future grain yields as a statistical sample.Then it constructs a damage function of climate change impact on grain yield.Thus,this study establishes 288 samples according to the results of highly relevant papers and applies Meta-analysis method to assess the impact of climate change on staple cropsyield of China.The results show that the impacts of climate change on the yield of staple crops are different depending on climate change scenarios,crop types,planting areas,and whether CO_2 fertilizer efficiency is considered.It concludes that for every 1℃ increase in temperature,the overall yield of Chinas three major food crops decreases by about 2.6%;for every 1% increase in precipitation,the yield of Chinas three major food crops increases by about 0.4%;while the influence of the CO_2 fertilizer effect has large uncertainty due to different studies.By synthesizing the effects of temperature and precipitation on grain yields,this study shows that climate change will certainly affect Chinas food security even offsetting the positive effects of some technological advance.Our study summarizes the impacts of climate change on the yield of staple crops under the same temperature and precipitation change and will provide reliable data basis for assessing the impact of climate change by using agricultural partial equilibrium model or integrated assessment models.