Assessing future climate trends and global warming requires the analysis of long and heterogeneous time series. Gaps in the data series, due to malfunctioning or poorly calibrated instrumentation, can introduce a bias that should be considered in the final assessment. Therefore data quality is a strategic issue for the reliability of this kind of studies and particularly missing data management strategy is a key aspect. This study focuses on uncertainty introduced on climate change trend analysis by the use of incomplete temperature time series recorded by weather stations. Two data quality approaches are evaluated: replacement or filling gap techniques by interpolation and exclusion criteria based on the maximum acceptable number of missing values in a time series. In this study the regression method based on a neighboring station to estimate missing data of another station has been chosen among several interpolation methods. Several complete temperature time series are used to simulate the occurrence of random and consecutive missing values and compare the uncertainty of trend estimation. The two methods are applied to the incomplete data series and the uncertainty in trend analysis results introduced by the two methods is evaluated and compared.