Abstract
Record-breaking hot and cold extremes have occurred in China in recent years and, therefore, it is compelling to investigate the long-term trend in temperature extremes at individual stations to see whether they have become more frequent. Many previous studies on the linear trend analysis of temperaure extremes in China have used oridinary least squares (OLS) regression, without consideration of non-Gaussian and/or serially dependent characteristics, or nonparametric methods, again not considering the latter, thus leaving some uncertainty in the significance testing. The present study examines in detail these characteristics in eight commonly used extreme temperature indices, on the basis of both station data and gridded data across China. The results show that 71–100% of the stations or grids cannot directly use standard OLS regression to analyze the statistical significance of the linear trend, because of either non-Gaussian or Gaussian but serially dependent characteristics in the regression residuals. Also, more than 43% of the stations and more than 54% of the grid boxes for annual indies cannot directly use the original Sen’s slope estimator and Mann–Kendall test because of serial dependence. Based on a nonparamtric method that takes into account serial dependence, the spatial patterns of the linear trend on an annual basis, as well as in hot and cold extremes, are examined for the period 1960–2017. The results show that hot extremes at most stations have increased, more than 57% of which are statistically significant; whereas, cold extremes at almost all stations have decreased, more than 32% (85%) of which are statistically significant during daytime (at night).
Highlights
In recent years, China has frequently witnessed recordbreaking temperature extremes
Indices based on absolute values (TXx etc.) should not be Gaussian if the block size is large enough, and percentile-based indices may be approximated by a Gaussian distribution if the daily temperature data are sufficiently independent
The annual TX90p index is taken as an example to illustrate the impact of non-Gaussian and/or serial dependent characteristics on the estimation of the linear trend slope and the corresponding statistical significance of the linear trend (Fig. 6)
Summary
China has frequently witnessed recordbreaking temperature extremes. For example, in 2013, extreme summer heat occurred in East China (Sun et al 2014; Zhou et al 2014; Qian 2016). Some studies have used nonparametric Kendall’s taubased Sen–Theil estimator, known as Sen’s (1968) slope estimator, to estimate the spatial pattern of the linear trend in climate extremes in China, along with the nonparametric Mann–Kendall test to assess the corresponding statistical significances for station data (e.g., Zhai and Pan 2003; You et al 2013; Chen and Zhai 2017; Lin et al 2017) or gridded data (e.g., Yin et al 2015) Both methods do not make assumptions about the underlying distribution of the climate indices. In the second part of the study, we compute the spatial patterns of the linear trends in temperature extremes using this method for the data updated to 2017
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