Abstract: Abstract: Agro-meteorological services can provide a strong guarantee for agricultural disaster prevention and reduction, national food security and sustainable development of agriculture. In this paper, we systematically described a Crop Growth Simulating and Monitoring System in China (CGMS-China). The system was established based on 4 crop models, i.e. WOFOST, Oryza2000, WheatSM, ChinaAgrosys. The CGMS-China could be applied to national agro-meteorological services. The CGMS-China includes database layer, model layer and application layer. In the model layer, 4 crop models were integrated by application program interface. They were driven by daily-scale data. The WOFOST model was for winter wheat and maize simulation, the WheatSM was for winter wheat simulation, Oryza2000 was for rice simulation and ChinaAgrosys was for remote sensing data assimilation. The data assimilation method included SCE-UA, particle swarm optimization, and so on. The CGMS-China was used for crop growth monitoring, agro-meteorological disaster assessment and crop yield forecast. The crop growth monitoring was based on leaf are index, aboveground biomass and dry weight of storage organs. The agro-meteorological disaster was estimated based on yield reducing rate. The yield could be predicted by relative yield prediction method based on aboveground biomass or yield in the CGMS-China system. The output of CGMS-China for crop growth monitoring could be used for comparison with those in last 5 years, last year, and normal year. The case study in Tailai, Heilongjiang and Fuxin, Liaoning showed that the CGMS-China was a reliable agro-meteorological service product with good quality for crop growth monitoring, crop yield forecast and yield loss assessments of agro-meteorological disasters. Crop growth assessment index was established using outputs of CGMS-China which included aboveground biomass, leaf area index and weight of storage organs. They were applied to real-time monitoring of wheat, maize and rice growth. The drought assessment was also conducted by the CGMS-China system. The CGMS-China performed well at yield loss assessment of spring maize caused by drought in the middle of August, 2014 and yield loss assessment of early rice caused by heat stress on the 22nd June, 2016. The comparison of real-time monitoring and simulating could well reflect the crop growth during the drought events. The CGMS-China was used to predict winter wheat yield in 2014 in China. The average forecast relative error was 7% and the relative error in most provinces (autonomous region) was less than 10%. In the meantime, application of remote sensing assimilation with crop model was also introduced in this paper. The relative error used CGMS-China combined with remote sensing data assimilation was less than 11% in Hongtong county, Shanxi province, China. Finally, we discussed the future directions of application of crop model in agro-meteorological services. In sum, the CGMS-China can provide services well in crop growth development simulation, meteorological disaster monitoring and yield prediction.