This study investigates trends in annual maximum daily precipitation data from 8,326 high-quality land-based observing stations over the period from 1900 to 2009. Two statistical methods—Mann–Kendall nonparametric trend test and nonstationary generalized extreme value analysis—are used to evaluate the presence of monotonic trends and the association with globally averaged near-surface temperature, respectively. The results show that nearly two-thirds of the stations exhibit increasing trends, which are statistically significant. The nonstationary extreme value analysis reveals a statistically significant positive association between annual maximum precipitation and global mean near-surface temperature, with a median intensity of extreme precipitation changing by 5.9% to 7.7% K−1. The sensitivity varies with latitude, being highest in the tropics and higher latitudes, and lowest around 13°S and 11°N. The study also highlights the importance of improving data collection in equatorial regions due to the uneven geographic distribution of stations.This study investigates trends in annual maximum daily precipitation data from 8,326 high-quality land-based observing stations over the period from 1900 to 2009. Two statistical methods—Mann–Kendall nonparametric trend test and nonstationary generalized extreme value analysis—are used to evaluate the presence of monotonic trends and the association with globally averaged near-surface temperature, respectively. The results show that nearly two-thirds of the stations exhibit increasing trends, which are statistically significant. The nonstationary extreme value analysis reveals a statistically significant positive association between annual maximum precipitation and global mean near-surface temperature, with a median intensity of extreme precipitation changing by 5.9% to 7.7% K−1. The sensitivity varies with latitude, being highest in the tropics and higher latitudes, and lowest around 13°S and 11°N. The study also highlights the importance of improving data collection in equatorial regions due to the uneven geographic distribution of stations.