LAUSR

Heat waves (HWs) are one of the current topics of scientific research in the context of global warming, as they can have a disastrous impact on both environment and society. Therefore, detailed studies of variations of HWs that assess variability on spatiotemporal scales are becoming increasingly necessary. The main objective of this study is to detect and analyse variation in HWs and potential climate drivers across China based on Köppen climate classifications. Daily observation data (maximum and minimum temperature) from 1961 to 2016 were used in the study to define HWs based on excess heat factors (EHFs). Linear trend analysis, Bayesian change‐point analysis, cross‐wavelet transform method, and Pearson correlation analysis method were adopted to identify the trends, variability and resonance periodicity of EHFs in sub‐regional, and the possible links between EHFs and large‐scale climate patterns, respectively. Results show that there was an overall significantly increasing trend of EHFs over six climatic zones except for HWM in the Polar tundra (ET). The spatiotemporal characteristics of EHFs in mainland China vary considerably. The spatial patterns of the mean annual EHFs are similar to their change trend. The spatial pattern of the number of individual HWs, the number of days that contribute to HWs, and the length of the longest HW in arid (BB), semi‐arid (BS) and cold semi‐humid (DW) areas of study is the west exceeding the east; temperate humid (CF) is contrary to that, with the south exceeding the north in temperate semi‐humid (CW) and ET. In addition, the mean temperature of all heatwaves (HWM) and the peak daily value in the hottest HW appeared in parts of northern DW, CF and BS. Change point first occurs in the northern and then in the southern region across mainland China, excepting HWM. A dominant positive correlation was found for Multivariate ENSO Index (MEI) and Arctic Oscillation Index (AOI) with EHFs, while a dominant negative correlation was detected for Pacific Decadal Oscillation (PDO) and the North Atlantic Oscillation (NAO) across China. A common significant 2–4 year periodical oscillation was detected in the EHFs series at the nationwide scale. Strengthening anti‐cyclone circulation in the Mongolian Plateau, and weakening monsoon flow and speed have contributed to the changes in EHFs across China.