[Objective]Grey relational analysis between cotton quality and climatic factors was conducted to provide references for dealing with climate changes, properly using climatic resources and effectively avoiding climate risks. [Method]Major climatic factors were monthly average temperature, precipitation and sunshine hours of the entire cotton growth period in 2010-2012. Level three cotton of the same year was used as the evaluation standard. Using image transformation method after the dimensionlessness, with monthly average temperature, precipitation and sunshine hours as input variables and cotton quality as output variable, the grey correlation analytical model was established. [Result]In 2010-2012, the sequence of the three climatic factors was monthly average precipitation>temperature>sunshine hours. Considering month, the most influential months of monthly average temperature and sunshine hours on cotton quality were from May to September. The most influential months of monthly precipitation on cotton quality were April, May, July and October. [Conclusion]The established model of grey relational analysis is applicable in practical cotton production, as it is an objective method for evaluating the effects of climatic factors on cotton quality.