Based on the innovative trend analysis(ITA)method, this paper improved the parametric test method of trend significance, proposed a quantitative index to describe the average trend of time series, and used the bootstrap method to test the significance of the trend. After the Monte-Carlo numerical simulation, the detection results of the new method on the artificial data sequences were compared with the classic Mann-Kendall(MK)method and ITA method respectively, from which its feasibility was verified. Finally, the new method was applied to the trend analysis of four kinds of time series with different lengths, in different regions, and about different hydro-meteorological elements. The results showed that at the 5% significance level, there was a significant increasing trend in series of the annual runoff in the upstream of Heihe River, the number of storm days per year in Fukuoka, Japan, and the annual average temperature in Qionghai. Meanwhile, there was a significant decreasing trend at a significant level of 10% in the maximum daily precipitation in Beijing.