Assessing spatiotemporal variation of heat waves during 1961–2016 across mainland China

Abstract

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

Anthropogenic influence on compound dry and hot events in China based on Coupled Model Intercomparison Project Phase 6 models

“…Over the course of recent decades, the odds of climate extremes, such as droughts and hot extremes, have changed widely (Meehl and Tebaldi, 2004; Dai, 2012; Perkins, 2015; Su et al ., 2020; Di Luca et al ., 2020a; Wu et al ., 2021), with substantial concomitant impacts on human society and ecosystems over different regions, including China (Lu et al ., 2016; Mora et al ., 2017; Sutanto et al ., 2020). Drought analysis based on different indicators has detected an overall increase in the frequency, duration, and intensity in China in the past few decades (Yu et al ., 2014; Shao et al ., 2018), which is accompanied by a fast increase in temperature‐related extremes (Sun et al ., 2014; 2017; Guo et al ., 2020). Recent droughts (e.g., 2006 in southwest China and 2013 in southern China) and heatwaves (e.g., 2013 in eastern China) have caused substantial socioeconomic losses (Sun et al ., 2014; Lu et al ., 2018; Ye et al ., 2018).…”

Coupling Deep Learning and Physically Based Hydrological Models for Monthly Streamflow Predictions

“…In addition, daily meteorological and streamflow data were aggregated to monthly values, and monthly meteorological data for the DJKR basin were calculated by averaging all grid points within the basin and surround. The (Cheng et al, 2019;Guo et al, 2020;Yang et al, 2021).…”

“…A few other indices including Southern Oscillation Index (SOI), Multivariate ENSO Index (MEI), Bivariate ENSO Time series (CENSO), Pacific Decadal Oscillation (PDO), and Quasi‐Biennial Oscillation (QBO) are also considered. Multiple studies have proven the link between above‐mentioned large‐scale climate indices and the hydroclimatology of the southern China (Cheng et al., 2019; Guo et al., 2020; Yang et al., 2021).…”