Trend damping is one of the biases of judgmental forecasting, which means that when people forecast from time series with noises, they always damp trends in data so that forecasts lie below upward trends but above downward trends. In other words, they underestimate the steepness of the series. Previous researches mainly focused on the effect of the characteristic or presentation of series on trend damping, but rarely cared about the situational factors, such as psychological distance. Present research focus on the effect of psychological distance and series characteristics (trend and slope) on trend damping in judgmental forecasting. In term of construal level theory(CLT), general trends convey a high-level, abstract rule about how the future will manifest itself, whereas deviation from trends represent a low-level, concrete exception to the rule. And CLT related researches suggested that different psychological distances may show similar influence on construal and psychological consequence. So we assumed that distant psychological distance may similar led to less trend damping, while near distance led to more trend damping. Three experiments was designed to explore the effect of psychological distance and series characteristics(trend and slope) on trend damping, while experiment 1 focused on spatial distance, experiment 2 cared about social distance, experiment 3 manipulated temporal distance. The materials were time series which were constructed using power-law functions, of the general form: y=100+300×（x/48）k, then add Gaussian noise with M=0, SD=10, where y represents number of pixels above the x-axis, x has the dimension time, and k controls the acceleration of the function. Downward trends were completely symmetric with upward ones, and the symmetry axis was y=250. The dependent variable was the D-value of predictive value and truth-value, true value minus predictive value for upward trend, predictive value minus true value for downward trend. When the dependent variable>0, trend damping was occurred, the bigger of the dependent variable, the larger of damping effect. The results showed that: 1) Individuals tended to damp trends when forecast. 2) The damping effect was greater when the trend was downward than upward, when the slope of time series was bigger. 3) The influence of different psychological distances on trend damping was different, the damping effect was not different between different spatial distances, but was smaller when forecast for others rather than for themselves, when forecast for temporal near situation rather than temporal distal. The no effect of spatial distance on trend damping may result from that spatial distance doesn’t affect the slope of prediction. As hypothesis showed, when forecasting for others rather than for themselves, individuals rely more on general trend, which result in less damping effect. But when forecasting for distant future, as the adaptation account of trend damping shows, individuals may rely more on the implicit negative acceleration mode which is the representation of the developing trend of natural series, which led to larger damping effect. In other words, the implicit negative acceleration mode of trend may be more abstract representation of the trend than the global trend, and when forecasting for distant future, individuals rely more on it.