Abstract: It is well known that global climate is warming over the past decades. And there are great uncertainties for the assessment of warming magnitude caused by meteorological data itself, different methods adopted, hypothesis of future emission scenario, and other factors as well. Ordinary least squares (OLS) is usually used to analyze the trend of climate change by fitting linear regression to the time series of meteorological observation. And the hypothesis of independence should be met by the random error of the time series, otherwise autocorrelation exists in the time series and uncertainties will appear in the results.Unfortunately research on the autocorrelation of climate change is weak. So taking the temperature observation (including mean temperature, maximum temperature and minimum temperature) of Pinghu meteorological station in Zhejiang Province during 1954—2004 as an example, the autocorrelation possibly existing in time series and its influence on the trend analysis of climate change are studied. And the method of Durbin-Watson test is introduced to check whether the autocorrelation exists or not in the time series during the utilization of OLS in the trend analysis of temperature change. And if so, the method of Cochrane-Orcutt is adopted to remove the autocorrelation. Analysis indicates that the warming trend is magnified by the existence of autocorrelation in Pinghu temperature time series, and the confidence level of warming trend is improperly increased, by which uncertainties of clim ate change is added. In a word, it is indispensable to check and remove the autocorrelation of climatic time series, and much more work should be done further to test the autocorrelation and its possible impacts on the uncertainties of warming forecasting at regional and global scale.