Abstract: The surface homogenization daily data from 85 meteorological stations was utilized to analyze the spatial and temporal distribution characteristics of annual average maximum（minimum）temperature,extreme minimum temperature,heat wave,cold wave and frost in north China from 1961to 2017.The results showed that:（1）The increase rate（0.45 ℃/10 a）of the annual average minimum temperature was higher than that the maximum temperature（0.27 ℃/10 a）,and the daily temperature range decreased at a rate of 0.18 ℃/10 a.The positive trend of maximum temperature in central and northern parts of Inner Mongolia and south-central Shanxi was relatively large.The minimum temperature increased more significantly in most of Inner Mongolia,northeastern Shanxi,central Hebei and Beijing-Tianjin areas.（2）The annual average days of heat wave showed a significant increasing trend（0.44 d/10 a）,especially after the 1990s.The large values of heat wave days were concentrated in southern and western north China.The number of heat wave days in most parts of north China displayed an increasing trend,except for the negative trend in parts of southern Hebei.（3）The average annual extreme minimum temperature showed a significant positive trend（0.53 ℃/10 a）,the average number of cold nights decreased significantly（-4.7 d/10 a）,the warm nights increased significantly（3.8 d/10 a）,and the cold days decreased（-2.4 d/10 a）.The annual average cold wave frequency decreased significantly（-0.5 times/10 a）with a spatial distribution of “more in the north and less in the south”.The average number of frost days decreased at a rate of 3.5 d/10 a,especially after the1980s.Except for some areas in northwestern Shanxi and southeastern Inner Mongolia,the frost days in most parts of north China showed a negative trend.（4）The comparison results of different sub-regions showed that Tianjin was the most sensitive to climate warming,followed by western Inner Mongolia.Shanxi was the least sensitive（especially to low temperature related indicators）,followed by Hebei（especially to high temperature related indicators）.The results were helpful to further understand the regional characteristics of temperature-related high-impact weather and climate events in north China.