Variation in climatic growing season indices in North China from 1960-2017 were analyzed based on daily temperature data. The methods of Mann-Kendall test, wavelet transforms and rescaled range (R/S) analysis were employed to delineate the rate of change, abrupt change points, statistical significance of the trends, periodicity, and future trends of growing season indices, including growing season start (GSS), growing season end (GSE), growing season length (GSL), active accumulated temperature ≥10℃ (AT10) and the days of active accumulated temperature ≥10℃(DT10). Important results were obtained as follows: 1) GSS, GSL, AT10 and DT10 showed significant change trend (P<0.05) at the rate of -2.43 d/10a, 2.95 d/10a, 67.14℃/10a and 2.31 d/10a, respectively. GSE presented a non-significant trend with the change rate at 0.53 d/10a. The GSL has extended 17.2 days during the last 60 years, mainly due to the advanced GSS evident in the spring (14.1 d). 2) Spatially, as for GSS, GSL, AT10 and DT10, there were 67.1%, 62.9%, 95.7%, and 92.9% stations showed significant trends (P<0.05). The spatial distribution patterns of the trends in GSS and GSL, AT10 and DT10 were similar. 3) The growth season indices showed obvious mutation in the mid-to-late 1990s. Abrupt change points of GSS, GSE, GSL, AT10 and DT10 were at 1995, 1995 and 2014, 1994, 1998, and 1997. 4 ) The wavelet analysis showed that there were two primary short periods of 2-3 years and 5-6 years for the oscillation of growing season indices in North China. Hurst indexes (H) of growing season indices were all greater than 0.7. It indicated that there will be obvious Hurst phenomenon in the future, and the past trends of GSS, GSE, GSL, AT10 and DT10 will continue in the future period. 5) GSS was negatively related to the atmospheric circulation index of North Atlantic Oscillation (NAO), West Pacific Pattern (WP), Atlantic Multidecadal Oscillation (AMO), and Eastern Atlantic Pattern (EA), and positively related to Polar/ Eurasia Pattern (POL). GSE was positively correlated with AMO. As for AT10, it positively related to AMO and EA, and negatively related to NAO and WP. AMO is the main atmospheric circulation factor affected the growth season indicators (GSS, GSE and AT10) in North China during 1960-2017.