Unprecedented Hot Extremes Observed in City Clusters in China during Summer 2022

2022年夏季罕见的极端高温侵袭中国城市群

+ Author Affiliations + Find other works by these authors
  • Corresponding author: Panmao ZHAI, pmzhai@cma.gov.cn
  • Funds:

    Supported by the China Meteorological Administration (CMA) Special Program on Climate Change and Chinese Academy of Meteorological Sciences (CAMS) Science Development Foundation: Novel Features and Mechanisms of China’s Regional Extreme Events

  • doi: 10.1007/s13351-023-2184-9

PDF

  • We report here extreme daytime and nighttime temperatures, severe heatwaves, and compound hot events recorded in China’s five most densely populated city clusters in summer 2022. New records were set, with daytime maximum temperatures > 42°C in cities along the Yangtze River valley and extreme nighttime temperatures > 30°C. Widespread prolonged heatwaves lasting for > 40 days and compound hot days occurring for > 32 consecutive days were experienced in these city clusters. To explore the possible causes of these extreme events, we analyzed the linkages between the changes in the mean temperatures and hot extremes for different-sized cities in the city clusters. We found that megacities (e.g., Beijing, Shanghai, Guangzhou, etc.) and large cities (e.g., Baoding, Wuxi, Foshan, etc.), especially those located in central and eastern China, experienced unprecedented extreme high temperatures, not only in the daytime but also at night. We observed large increases in the mean temperatures and more frequent and more intense hot extremes in cities affected by both the background global warming and intensified urbanization. Megacities and large cities experienced higher and more frequent extreme temperatures and greater warming trends than medium- and small-sized cities (e.g., Zhangjiakou, Zhenjiang, Yaan, etc.). The evidence of the dependence of temperature trends on a city’s size shows that intensified heat island effects may increase the threat of hot extremes in cities undergoing rapid urbanization.
    利用最新的气象观测资料,本文揭示了2022年夏季中国五个主要城市群遭遇到的前所未有的极端高温天气特征。此次极端高温过程打破了多项历史记录,不仅在白天出现极端高温,夜间也出现极端高温。长江流域一些城市的白天最高温度超过42℃,夜间极端温度超过30℃,高温热浪影响时间超过40天,日夜复合型极端高温事件超过32天。进一步的研究发现,受到全球变暖和城市化共同影响,中国中东部的特大城市和大城市相比中小城市,平均温度和极端高温增温趋势更为迅猛。显然,快速的城市化引起的城市热岛效应的加剧给特大城市和大城市带来了更强、更频繁的极端高温事件。本文研究结果为认识和应对城市极端高温热浪风险提供了科学依据。
  • 加载中
  • Fig. 1.  Spatial distributions of (a) TXx, (b) TNx, (c) the number of heatwave days, and (d) the number of compound hot days over China in 2022. The black dashed ellipse areas indicate the five city clusters in this study. We used the Cressman interpolation technique to interpolate the station-based data and indices on 1° × 1° grids.

    Fig. 2.  Changes in the number of heatwave days in China’s five megacities from 1961 to 2022. The size of the colored circles represents the intensity: the larger the size, the stronger the intensity.

    Fig. 3.  Changes in the summer mean surface air temperature in megacities (red lines), large cities (orange lines), and medium- and small-sized cities (light blue lines) in China’s five city clusters from 1961 to 2022.

    Fig. 4.  Linkage between the trends in the summer mean temperatures and numbers of (a) hot days, (b) hot nights, (c) compound hot days, (d) the daytime TXx, and (e) the nighttime TNx in different sized cities. The time period for the trend estimation is 1961–2022 (MC: megacities; LC: large cities; M&SC: medium- and small-sized cities).

    Table 1.  Definitions of hot extremes

    Hot extremesDefinition
    TXxSummer maximum value of the daily maximum temperature (Tmax; °C)
    TNxSummer maximum value of the daily minimum temperature (Tmin; °C)
    Hot dayA day on which Tmax exceeds the 95th percentile threshold (day)
    Hot nightA night on which Tmin exceeds the 95th percentile threshold (day)
    Compound hot dayA day on which a hot day is followed in sequence by a hot night (day)
    HeatwaveProlonged number of days with Tmax > 35°C (day)
    Download: Download as CSV

    Table 2.  Comparison of hot extremes between historical records during the time period 1961–2021 and the year 2022 in the selected nine megacities in China. Bold values marked with an asterisk indicate that the values are greater than or equal to the corresponding historical record. Year of historical record is given in parentheses

    Megacity (station ID)Historical record (1961–2021)Year 2022
    TXx (°C)TNx (°C)No. of heatwave daysNo. of compound hot daysTXx (°C)TNx (°C)No. of heatwave daysNo. of compound hot days
    Beijing (54511)41.9 (1999)29.2 (2010)26 (2000)14 (2018)39.228.4157
    Tianjin (54527)40.5 (2000)29.4 (2018)25 (2018)15 (2018)40.128.4145
    Shanghai (58367)40.9 (2017)32.1 (2010)47 (2013)34 (2013)40.9*31.649*36*
    Hangzhou (58457)41.6 (2013)30.7 (2010)51 (2013)28 (2013)41.8*31.3*56*32*
    Chengdu (56295)40.3 (2006)29.4 (2016)35 (2006)21 (2006)43.4*32.4*51*41*
    Chongqing (57511)44.3 (2006)32.8 (1964)58 (2006)25 (2006)45*34.5*59*30*
    Wuhan (57494)39.7 (2017)32.3 (2003)44 (1961)13 (2013)39.7*31.648*22*
    Guangzhou (59287)39.1 (2004)30.4 (2015)35 (2006)23 (2020)38.129.52618
    Shenzhen (59493)38.7 (1980)30.3 (2005)10 (1998)20 (2000)36.228.9911
    Download: Download as CSV

    Table 3.  Average number of heatwave days and compound hot days in megacities, large cities, and medium- and small-sized cities from the five city clusters during the historical time period 1961–1990 and the time period 1991–2022. Bold values marked with an asterisk indicate that the difference is statistically significant at the 0.05 level (MC: megacities; LC: large cities; M&SC: medium- and small-sized cities)

    City clusterCity classificationAverage number of days > 35°C during 1961–1990 (P1)Average number of days > 35°C during 1991–2022 (P2)Difference in the average number of days > 35°C (P2 − P1)Average number of compound hot days during 1961–1990 (P1)Average number of compound hot days during 1991–2022 (P2)Difference in the number of compound hot days (P2 − P1)
    BTHMC5105*1.23.82.6*
    LC79213.22.2*
    M&SC3520.62.11.5
    YZRDMC132411*2.611.58.9*
    LC10166*2.48.76.3*
    M&SC9156*1.77.35.6*
    PRDMC4128*1.412.310.9*
    LC61041.411.510.1*
    M&SC3520.99.58.6*
    SCRMC15227*1.95.23.3*
    LC10155*1.64.73.1*
    M&SC6931.54.32.7*
    WHRMC17225*1.63.51.9*
    LC151611.53.11.6
    M&SC111321.32.81.5
    Download: Download as CSV
  • [1]

    Bader, D. A., R. Blake, A. Grimm, et al., 2018: Urban climate science. Climate Change and Cities: Second Assessment Report of the Urban Climate Change Research Network, C. Rosenzweig, W. D. Solecki, P. Romero-Lankao, et al., Eds., Cambridge University Press, Cambridge, United Kingdom, 27–60, doi: 10.1017/9781316563878.009.
    [2]

    Chen, X. L., and T. J. Zhou, 2018: Relative contributions of external SST forcing and internal atmospheric variability to July–August heat waves over the Yangtze River valley. Climate Dyn., 51, 4403–4419. doi: 10.1007/s00382-017-3871-y.
    [3]

    Chen, Y., and P. M. Zhai, 2017: Revisiting summertime hot extremes in China during 1961–2015: Overlooked compound extremes and significant changes. Geophys. Res. Lett., 44, 5096–5103. doi: 10.1002/2016GL072281.
    [4]

    Deng, K. Q., S. Yang, D. J. Gu, et al., 2020: Record-breaking heat wave in southern China and delayed onset of South China Sea summer monsoon driven by the Pacific subtropical high. Climate Dyn., 54, 3751–3764. doi: 10.1007/s00382-020-05203-8.
    [5]

    He, G. H., Y. J. Xu, Z. L. Hou, et al., 2021: The assessment of current mortality burden and future mortality risk attributable to compound hot extremes in China. Sci. Total Environ., 777, 146219. doi: 10.1016/j.scitotenv.2021.146219.
    [6]

    IPCC, 2021: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, V. Masson-Delmotte, P. M. Zhai, A. Pirani, et al., Eds., Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 3–32.
    [7]

    Jones, B., and B. C. O’Neill, 2016: Spatially explicit global population scenarios consistent with the Shared Socioeconomic Pathways. Environ. Res. Lett., 11, 084003. doi: 10.1088/1748-9326/11/8/084003.
    [8]

    Kong, Q. Q., S. B. Guerreiro, S. Blenkinsop, et al., 2020: Increases in summertime concurrent drought and heatwave in Eastern China. Wea. Climate Extrem., 28, 100242. doi: 10.1016/j.wace.2019.100242.
    [9]

    Kuang, W. H., G. M. Du, D. S. Lu, et al., 2021: Global observation of urban expansion and land-cover dynamics using satellite big-data. Sci. Bull., 66, 297–300. doi: 10.1016/j.scib.2020.10.022.
    [10]

    Li, Q. X., and W. J. Dong, 2009: Detection and adjustment of undocumented discontinuities in Chinese temperature series using a composite approach. Adv. Atmos. Sci., 26, 143–153. doi: 10.1007/s00376-009-0143-8.
    [11]

    Liu, X. C., Q. H. Tang, X. J. Zhang, et al., 2018: Projected changes in extreme high temperature and heat stress in China. J. Meteor. Res., 32, 351–366. doi: 10.1007/s13351-018-7120-z.
    [12]

    Lu, R. Y., K. Xu, R. D. Chen, et al., 2023: Heat waves in summer 2022 and increasing concern regarding heat waves in general. Atmos. Ocean. Sci. Lett., 16, 100290. doi: 10.1016/J.AOSL.2022.100290.
    [13]

    Luo, M., and N.-C. Lau, 2017: Heat waves in southern China: Synoptic behavior, long-term change, and urbanization effects. J. Climate, 30, 703–720. doi: 10.1175/JCLI-D-16-0269.1.
    [14]

    Luo, M., and N.-C. Lau, 2019: Amplifying effect of ENSO on heat waves in China. Climate Dyn., 52, 3277–3289. doi: 10.1007/s00382-018-4322-0.
    [15]

    Ma, F., and X. Yuan, 2021: More persistent summer compound hot extremes caused by global urbanization. Geophys. Res. Lett., 48, e2021GL093721. doi: 10.1029/2021GL093721.
    [16]

    Mora, C., T. McKenzie, I. M. Gaw, et al., 2022: Over half of known human pathogenic diseases can be aggravated by climate change. Nat. Climate Change, 12, 869–875. doi: 10.1038/s41558-022-01426-1.
    [17]

    Ren, G. Y., L. Zhang, T. Bian, et al., 2015: Urbanization effect on change of daily temperature at Shijiazhuang weather station. Chinese J. Geophys., 58, 398–410. doi: 10.6038/cjg20150205. (in Chinese)
    [18]

    Ren, Y. Y., D. Parker, G. Y. Ren, et al., 2016: Tempo-spatial characteristics of sub-daily temperature trends in mainland China. Climate Dyn., 46, 2737–2748. doi: 10.1007/s00382-015-2726-7.
    [19]

    Sen, P. K., 1968: Estimates of the regression coefficient based on Kendall’s tau. J. Amer. Stat. Assoc., 63, 1379–1389. doi: 10.1080/01621459.1968.10480934.
    [20]

    Shi, Z. T., G. S. Jia, Y. H. Hu, et al., 2019: The contribution of intensified urbanization effects on surface warming trends in China. Theor. Appl. Climatol., 138, 1125–1137. doi: 10.1007/s00704-019-02892-y.
    [21]

    Sun, Y., X. B. Zhang, G. Y. Ren, et al., 2016: Contribution of urbanization to warming in China. Nat. Climate Change, 6, 706–709. doi: 10.1038/nclimate2956.
    [22]

    Wang, J., Y. Chen, S. F. B. Tett, et al., 2020: Anthropogenically-driven increases in the risks of summertime compound hot extremes. Nat. Commun., 11, 528. doi: 10.1038/s41467-019-14233-8.
    [23]

    Wang, J., Y. Chen, W. L. Liao, et al., 2021: Anthropogenic emissions and urbanization increase risk of compound hot extremes in cities. Nat. Climate Change, 11, 1084–1089. doi: 10.1038/s41558-021-01196-2.
    [24]

    Witze, A., 2022: Extreme heatwaves: surprising lessons from the record warmth. Nature, 608, 464–465. doi: 10.1038/d41586-022-02114-y.
    [25]

    Yang, Y., C. X. Jin, and S. Ali, 2020: Projection of heat wave in China under global warming targets of 1.5 °C and 2 °C by the ISIMIP models. Atmos. Res., 244, 105057. doi: 10.1016/j.atmosres.2020.105057.
    [26]

    Yu, R., P. M. Zhai, and Y. Chen, 2018: Facing climate change-related extreme events in megacities of China in the context of 1.5°C global warming. Curr. Opin. Environ. Sustain., 30, 75–81. doi: 10.1016/j.cosust.2018.03.008.
    [27]

    Zhai, P. M., Y. F. Yuan, R. Yu, et al., 2019: Climate change and sustainable development for cities. Chinese Sci. Bull., 64, 1995–2001. doi: 10.1360/N972018-00911. (in Chinese)
    [28]

    Zhang, X. B., and F. W. Zwiers, 2004: Comment on “Applicability of prewhitening to eliminate the influence of serial correlation on the Mann-Kendall test” by Sheng Yue and Chun Yuan Wang. Water Resour. Res., 40, W03805. doi: 10.1029/2003WR002073.
    [29]

    Zhang, X. B., L. Alexander, G. C. Hegerl, et al., 2011: Indices for monitoring changes in extremes based on daily temperature and precipitation data. WIREs Climate Change, 2, 851–870. doi: 10.1002/wcc.147.
    [30]

    Zhou, C. L., K. C. Wang, D. Qi, et al., 2019: Attribution of a record-breaking heatwave event in summer 2017 over the Yangtze River delta. Bull. Amer. Meteor. Soc., 100, S97–S103. doi: 10.1175/BAMS-D-18-0134.1.
  • Panmao ZHAI and Yufeng YUAN.pdf

  • 加载中
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Unprecedented Hot Extremes Observed in City Clusters in China during Summer 2022

    Corresponding author: Panmao ZHAI, pmzhai@cma.gov.cn
  • 1. Key Laboratory of Meteorological Disaster of Ministry of Education, Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing 210044
  • 2. State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, China Meteorological Administration, Beijing 100081
Funds: Supported by the China Meteorological Administration (CMA) Special Program on Climate Change and Chinese Academy of Meteorological Sciences (CAMS) Science Development Foundation: Novel Features and Mechanisms of China’s Regional Extreme Events

Abstract: We report here extreme daytime and nighttime temperatures, severe heatwaves, and compound hot events recorded in China’s five most densely populated city clusters in summer 2022. New records were set, with daytime maximum temperatures > 42°C in cities along the Yangtze River valley and extreme nighttime temperatures > 30°C. Widespread prolonged heatwaves lasting for > 40 days and compound hot days occurring for > 32 consecutive days were experienced in these city clusters. To explore the possible causes of these extreme events, we analyzed the linkages between the changes in the mean temperatures and hot extremes for different-sized cities in the city clusters. We found that megacities (e.g., Beijing, Shanghai, Guangzhou, etc.) and large cities (e.g., Baoding, Wuxi, Foshan, etc.), especially those located in central and eastern China, experienced unprecedented extreme high temperatures, not only in the daytime but also at night. We observed large increases in the mean temperatures and more frequent and more intense hot extremes in cities affected by both the background global warming and intensified urbanization. Megacities and large cities experienced higher and more frequent extreme temperatures and greater warming trends than medium- and small-sized cities (e.g., Zhangjiakou, Zhenjiang, Yaan, etc.). The evidence of the dependence of temperature trends on a city’s size shows that intensified heat island effects may increase the threat of hot extremes in cities undergoing rapid urbanization.

2022年夏季罕见的极端高温侵袭中国城市群

利用最新的气象观测资料,本文揭示了2022年夏季中国五个主要城市群遭遇到的前所未有的极端高温天气特征。此次极端高温过程打破了多项历史记录,不仅在白天出现极端高温,夜间也出现极端高温。长江流域一些城市的白天最高温度超过42℃,夜间极端温度超过30℃,高温热浪影响时间超过40天,日夜复合型极端高温事件超过32天。进一步的研究发现,受到全球变暖和城市化共同影响,中国中东部的特大城市和大城市相比中小城市,平均温度和极端高温增温趋势更为迅猛。显然,快速的城市化引起的城市热岛效应的加剧给特大城市和大城市带来了更强、更频繁的极端高温事件。本文研究结果为认识和应对城市极端高温热浪风险提供了科学依据。
    • Persistent high temperatures and heatwaves not only threaten human health, but can also fuel wildfires and reduce grain yields (Mora et al., 2022). Extreme heatwaves affected many parts of the Northern Hemisphere in summer 2022, killing tens of thousands of people in Spain, France, and the USA. Many parts of China, especially the central and eastern regions, also experienced widespread extreme high temperatures and unprecedented heatwaves, which are becoming more intense and more frequent than previously predicted (Witze, 2022). Recent studies have suggested that the extreme heatwaves in China in summer 2022 can be explained by an extreme anomaly of the subtropical high in the Northern Hemisphere (Lu et al., 2023). The interplay between global warming and the interannual variability of the El Niño–Southern Oscillation (ENSO) can also influence extreme heatwaves on a regional scale (Luo and Lau, 2019). On a smaller scale, the superimposed effects of urbanization on global warming will accelerate hot extreme events (Ma and Yuan, 2021).

      Heatwaves in China have become an important topic of research. China has experienced an increase in both the frequency and intensity of heatwaves in recent years as a result of global warming, compounded by droughts, ENSO, and other factors. This increase has been seen in both observations and model studies (Luo and Lau, 2017; Chen and Zhou, 2018; Kong et al., 2020; Yang et al., 2020). Extremely high temperatures and heatwaves in cities are of particular concern to policy-makers and the general public because densely populated cities have an additional influence on global warming and expose high numbers of people to extreme heat.

      The Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR6; IPCC, 2021) consolidated assessments of changes in urban weather and extreme climate events under the effects of global warming and stated that urbanization has significantly exacerbated hot extremes in cities. China has the largest population in the world, over half of whom live in cities. In some megacities, such as Beijing and Shanghai, the resident population is > 20 million, while the resident population in many other large cities exceeds 10 million. Depending on their size, building structures, and the underlying surface conditions, cities have large implications for local climate change (Ren et al., 2015; Bader et al., 2018; Kuang et al., 2021). Although the quantitative effects of urbanization on large-scale warming are still debated (Sun et al., 2016), it is accepted that urbanization effects have an important role in enhancing warming in city clusters, contributing about 10% of the warming in China’s city clusters (Shi et al., 2019), and exacerbating hot extremes. The contribution of urbanization effects may be > 50% in record-breaking extreme high temperature events (Zhou et al., 2019).

      Considering this recent research, especially publications about the global and regional-scale effects of the 2022 heatwave, we focused on the values and trends of hot extremes in China’s five most densely populated city clusters under the background of the widespread record-breaking extreme high temperatures in 2022. We aimed to understand the contribution of urbanization to changes in hot extremes in China’s cities and to determine the possible causes of extreme high temperatures in urban regions.

    2.   Data and methods
    • We focused on hot extremes in summer (June–August). We obtained the daily surface air temperatures for 1961–2022 from a dataset of 2481 stations provided by the National Meteorological Information Centre of the China Meteorological Administration. This dataset has been recommended for evaluating temperature extremes in China (Li and Dong, 2009). After strict quality control measures, 1240 stations were retained for this study. Stations were excluded if the rate of missing values in the annual data series was > 5%.

      To analyze the effects of city size on hot extremes, we classified the selected cities into three categories according to population and the method of classifying city populations in China (Jones and O’Neill, 2016). Cities with a population of > 10 million were defined as megacities, cities with a population of 5–10 million were defined as large cities, and cities with a population < 5 million were defined as medium- and small-sized cities.

      China has undergone rapid urbanization during the last four decades. We considered five city clusters in the Beijing–Tianjin–Hebei (BTH) region, the Yangtze River Delta (YZRD), the Pearl River Delta (PRD), the Sichuan–Chongqing region (SCR), and the Wuhan region (WHR). Each city cluster included one to two megacities, large cities, and medium- and small-sized cities.

      We focused on the high-impact features of hot extremes, including their intensity, frequency, and duration. Table 1 lists several relevant hot extreme indices and their definitions. Apart from the terms “compound hot day” and “heatwave,” the definitions of hot extremes referred to the Expert Team on Climate Change Detection and Indices (ETCCDI; Zhang et al., 2011). Trends in the frequency and intensity of hot extremes were detected by using Kendall’s tau-based slope estimator (Sen, 1968). Any possible autocorrelation in the time series was removed by using an iterative procedure, which has been demonstrated to be suitable for trend detection by the Monte Carlo simulation (Zhang and Zwiers, 2004).

      Hot extremesDefinition
      TXxSummer maximum value of the daily maximum temperature (Tmax; °C)
      TNxSummer maximum value of the daily minimum temperature (Tmin; °C)
      Hot dayA day on which Tmax exceeds the 95th percentile threshold (day)
      Hot nightA night on which Tmin exceeds the 95th percentile threshold (day)
      Compound hot dayA day on which a hot day is followed in sequence by a hot night (day)
      HeatwaveProlonged number of days with Tmax > 35°C (day)

      Table 1.  Definitions of hot extremes

    3.   Results
    • Extreme high temperatures were recorded over much of China in summer 2022 (Witze, 2022). Extreme high temperatures were observed in many parts of central and eastern China, especially in large cities. We did not include the western regions of China in this study because the cities and populations are smaller in these regions.

      Figure 1 shows our in situ observations of the hot extremes, including the summer maximum value of the daily maximum temperature (TXx), the summer maximum value of the daily minimum temperature (TNx), the number of heatwave days, and the number of compound hot days. Daytime extreme high temperatures > 35°C covered most of central and eastern China and temperatures > 40°C occurred in the YZRD, WHR, and parts of southern BTH. The maximum temperature in the SCR was > 42°C (Fig. 1a). The PRD experienced extreme high temperatures > 38°C.

      Figure 1.  Spatial distributions of (a) TXx, (b) TNx, (c) the number of heatwave days, and (d) the number of compound hot days over China in 2022. The black dashed ellipse areas indicate the five city clusters in this study. We used the Cressman interpolation technique to interpolate the station-based data and indices on 1° × 1° grids.

      In addition to the high values of TXx normally perceived in the daytime, TNx at night cannot be ignored. The distribution of TNx shown in Fig. 1b suggests that the maximum nighttime temperatures > 30°C were recorded in the YZRD, SCR, and WHR, and > 28°C in the PRD and BTH region. There were > 40 heatwave days in the SCR, WHR, and YZRD in summer 2022 (Fig. 1c).

      Compound hot days with a sequence of daytime/nighttime hot extremes have severe impacts on human health (Chen and Zhai, 2017; He et al., 2021; Wang et al., 2021). In summer 2022, the YZRD was the region most frequently influenced by compound hot extremes. There were > 32, > 28, and > 20 compound hot days in the SCR, YZRD, and WHR, respectively.

      We focused on nine megacities. Figure S1 and Table S1 show the distribution of the selected stations and the corresponding city information. We compared the record-breaking hot extremes of each megacity between the historical time period 1961–2021 and the year 2022 (Table 2). Our results show that Shanghai, Hangzhou, Chongqing, Chengdu, and Wuhan all broke historical temperature records in 2022, including TXx in the daytime, TNx in the nighttime, the number of heatwave days or compound hot days. The hot extremes in Beijing, Tianjin, Guangzhou, and Shenzhen ranked high in the historical values, although historical records were not broken. Ren et al. (2016) reported that cities release a large amount of heat at night when the heat island effect is much stronger than in the day, leading to higher temperatures at night and more compound hot extremes in cities. Wang et al. (2021) suggested that this increase in compound hot days in urban areas of eastern China is attributable to the increase in greenhouse gas emissions and urbanization.

      Megacity (station ID)Historical record (1961–2021)Year 2022
      TXx (°C)TNx (°C)No. of heatwave daysNo. of compound hot daysTXx (°C)TNx (°C)No. of heatwave daysNo. of compound hot days
      Beijing (54511)41.9 (1999)29.2 (2010)26 (2000)14 (2018)39.228.4157
      Tianjin (54527)40.5 (2000)29.4 (2018)25 (2018)15 (2018)40.128.4145
      Shanghai (58367)40.9 (2017)32.1 (2010)47 (2013)34 (2013)40.9*31.649*36*
      Hangzhou (58457)41.6 (2013)30.7 (2010)51 (2013)28 (2013)41.8*31.3*56*32*
      Chengdu (56295)40.3 (2006)29.4 (2016)35 (2006)21 (2006)43.4*32.4*51*41*
      Chongqing (57511)44.3 (2006)32.8 (1964)58 (2006)25 (2006)45*34.5*59*30*
      Wuhan (57494)39.7 (2017)32.3 (2003)44 (1961)13 (2013)39.7*31.648*22*
      Guangzhou (59287)39.1 (2004)30.4 (2015)35 (2006)23 (2020)38.129.52618
      Shenzhen (59493)38.7 (1980)30.3 (2005)10 (1998)20 (2000)36.228.9911

      Table 2.  Comparison of hot extremes between historical records during the time period 1961–2021 and the year 2022 in the selected nine megacities in China. Bold values marked with an asterisk indicate that the values are greater than or equal to the corresponding historical record. Year of historical record is given in parentheses

    • China’s cities are facing increasingly frequent and intense heatwaves under global warming. Cities have had an additional warming influence on hot extremes as a result of the superimposed urbanization effect. To understand the effect of the background of urbanization on changes in hot extremes, we analyzed the historical changes in the frequency and intensity of hot extremes and mean temperatures in different sized cities from five city clusters and investigated possible linkages.

      Figure 2 shows the changes in the frequency of heatwaves in five of China’s megacities (chosen from the corresponding city cluster). Increasing trends in the number of heatwave days over the last six decades are evident in Beijing, Guangzhou, and Shanghai; the intensity of heatwaves has clearly amplified since the 1990s. A remarkably increased frequency and greater intensity of heatwaves have been observed in Chongqing and Wuhan since the 1980s, although there has been an interdecadal variation during the last six decades. More frequent and stronger heatwaves are projected to affect China’s cities in the near future (Liu et al., 2018; Yu et al., 2018).

      Figure 2.  Changes in the number of heatwave days in China’s five megacities from 1961 to 2022. The size of the colored circles represents the intensity: the larger the size, the stronger the intensity.

      The urbanization effects were also non-negligible for local warming and the exacerbation of hot extremes. To reflect the effects of urbanization and background warming, we calculated the changes in the summer mean temperature for different city scales in the five city clusters from 1961 to 2022 (Fig. 3). Figure S1 shows the distribution of selected stations (including the station number and classification) and Table S1 provides information on the population of the cities. Figure 3 shows that the changes in the mean summer temperatures in the five major city clusters were similar, with a clear increasing trend. Megacities and large cities have experienced higher temperatures and greater warming trends than medium- and small-sized cities due to the intensifying urbanization effect. The enhanced heat island effect has an important role in amplifying warming in megacities and large cities. The warming trend in large cities has become increasingly obvious since the 1990s, especially in the BTH region, YZRD, PRD, and WHR. These warming amplification characteristics coincide with China’s urban development (Zhai et al., 2019).

      Figure 3.  Changes in the summer mean surface air temperature in megacities (red lines), large cities (orange lines), and medium- and small-sized cities (light blue lines) in China’s five city clusters from 1961 to 2022.

      During 1961–2022, the mean trends in temperature changes of megacities in the BTH region, YZRD, PRD, SCR, and WHR were 0.28, 0.33, 0.3, 0.14, and 0.28°C decade−1, respectively. For large cities, the mean trends in temperature changes in these regions were 0.21, 0.24, 0.17, 0.11, and 0.24°C decade−1, respectively. For medium- and small-sized cities, the mean trends in temperature changes in these regions were 0.16, 0.18, 0.14, 0.09, and 0.12°C decade−1, respectively. The differences in the warming trends between large and small-sized cities were from 0.05 to 0.16°C decade−1. This quantitative estimation can largely be attributed to the intensification of the urbanization effect of China’s cities. The warming trend in medium- and small-sized cities is more likely to be affected by background warming, whereas megacities and large cities are affected by both background warming and intensified urbanization effects. The phenomenon of trend dependence on city size reflects the intensified heat island effect.

      Table 3 shows the average number of days with hot extreme events for megacities, large cities, and medium- and small-sized cities in recent decades (1991–2022), the earlier reference period (1961–1990), and their differences. In general, the cities along the Yangtze River (e.g., in the YZRD, WHR, and SCR) experienced more heatwaves and compound hot days than those in the BTH region and PRD due to the influence of the background climate. During 1961–1990, the average number of hot extreme events in the five major city clusters was relatively small. However, the number of heatwave days and compound hot days increased significantly in the five city clusters during 1991–2022 when there was rapid urbanization. The megacities and large cities in all of the city clusters experienced a larger increase in heatwave days and compound hot days than the medium- and small-sized cities. These results suggest that the rapid urbanization during the recent decades has had an important role in the increase in local hot extremes.

      City clusterCity classificationAverage number of days > 35°C during 1961–1990 (P1)Average number of days > 35°C during 1991–2022 (P2)Difference in the average number of days > 35°C (P2 − P1)Average number of compound hot days during 1961–1990 (P1)Average number of compound hot days during 1991–2022 (P2)Difference in the number of compound hot days (P2 − P1)
      BTHMC5105*1.23.82.6*
      LC79213.22.2*
      M&SC3520.62.11.5
      YZRDMC132411*2.611.58.9*
      LC10166*2.48.76.3*
      M&SC9156*1.77.35.6*
      PRDMC4128*1.412.310.9*
      LC61041.411.510.1*
      M&SC3520.99.58.6*
      SCRMC15227*1.95.23.3*
      LC10155*1.64.73.1*
      M&SC6931.54.32.7*
      WHRMC17225*1.63.51.9*
      LC151611.53.11.6
      M&SC111321.32.81.5

      Table 3.  Average number of heatwave days and compound hot days in megacities, large cities, and medium- and small-sized cities from the five city clusters during the historical time period 1961–1990 and the time period 1991–2022. Bold values marked with an asterisk indicate that the difference is statistically significant at the 0.05 level (MC: megacities; LC: large cities; M&SC: medium- and small-sized cities)

      Background warming and increasingly intensified heat island effects have triggered more hot extremes in China’s cities in recent decades (Fig. 4). Figures 4a–c show the trends in the frequency of hot days, hot nights, and compound hot events associated with enhanced urbanization effects (as reflected by the trends in the mean temperatures) for different sized cities from the five city clusters. The YZRD and PRD regions had larger and more obviously increasing trends in hot extreme events than the BTH region, SCR, and WHR. This regional difference is probably because the YZRD and PRD are more affected by an intensified Northwest Pacific subtropical high (Luo and Lau, 2017), water vapor feedback related to the South China Sea summer monsoon (Deng et al., 2020), and land–atmosphere interactions (Chen and Zhou, 2018; Wang et al., 2020).

      Figure 4.  Linkage between the trends in the summer mean temperatures and numbers of (a) hot days, (b) hot nights, (c) compound hot days, (d) the daytime TXx, and (e) the nighttime TNx in different sized cities. The time period for the trend estimation is 1961–2022 (MC: megacities; LC: large cities; M&SC: medium- and small-sized cities).

      All the megacities and large cities in the five city clusters showed greater increasing trends of hot days, hot nights, and compound hot days than the medium- and small-sized cities. For hot days, for each 0.1°C decade−1 increase in the trend of the mean temperature, the trend in the numbers of hot days increased by 0.4, 1.24, 1.67, 1.21, and 0.46 days decade−1 in the megacities located in the BTH region, YZRD, PRD, SCR, and WHR, respectively. For hot nights, for each 0.1°C decade−1 increase in the trend in the mean temperature, the trend in the increase in frequency of hot nights was 0.82, 1.62, 1.81, 1.7, and 0.75 days decade−1 in megacities in the BTH region, YZRD, PRD, SCR, and WHR, respectively. For compound hot days, the increasing trends in megacities were 0.31, 1.14, 1.41, 1.2, and 0.5 days decade−1 in the BTH region, YZRD, PRD, SCR, and WHR, respectively. The increasing trends of hot extremes in the five city clusters during 1991–2022 were similar to the trends during 1961–2022, but showed more obvious trends as a result of rapid urbanization since the 1990s.

      Figures 4d and 4e show similar results for the trends in the intensity of TXx and TNx. For all five city clusters, rapid urbanization was reflected by the rapid increase in the mean temperature. The increasing trends in the intensity of TXx and TNx in megacities and large cities were clearly greater than the trends in medium- and small-sized cities. It should be emphasized that nighttime hot extremes are more affected by urbanization effects (Fig. 4e). Increasingly frequent and stronger hot extremes are therefore associated with faster warming in larger cities.

    4.   Summary and discussion
    • Our study has shown the unique features of hot extremes in China’s main city clusters during the intense Northern Hemisphere heatwave in summer 2022. We have provided evidence of record-breaking hot extremes in China’s main cities, especially along the Yangtze River valley, and have discussed the influence of enhanced urbanization on hot extremes in city clusters. Our conclusions are as follows.

      (1) New records were established for maximum daytime temperatures > 42°C and extremely high nighttime temperatures > 30°C in cities along the Yangtze River in summer 2022. Widespread prolonged heatwaves lasting for > 40 days with > 32 compound hot days were experienced in the five city clusters.

      (2) As global warming intensifies, many places are facing more frequent and stronger extreme heatwaves. Megacities and large cities have witnessed higher temperatures and more obvious warming trends than medium- and small-sized cities as a result of enhanced urbanization effects. Global warming and intensifying urban heat island effects amplify extreme high temperatures and have triggered more hot extremes in megacities and large cities, especially at night.

      (3) Record-breaking high temperatures and the number of heatwave days were recorded in China’s major city clusters in summer 2022 along the Yangtze River, but not in the PRD or BTH region. This suggests that regional climate anomalies relevant to interannual variabilities or influenced by the atmospheric circulation also have an important role in the regional features of changes in hot extremes in different cities. The trends in the mean temperature and hot extremes are different in different city clusters. Within the same city cluster, the difference between large and small cities largely reflects the impact of urbanization effects. Nevertheless, the quantitative separation of the effects of urbanization and other factors such as global warming requires further study.

      Although the proportion of urban areas relative to the total land area is small, cities contain > 59% of the world’s population. Dense populations increase exposure and further increase the risk of hot extremes. More effective action and scientific measures to address urban climate change need to be implemented to enable us to cope with the increasingly frequent and intense hot extremes in cities, to reduce economic losses, and to protect people’s health and safety.

      Acknowledgments. We thank the Editor and reviewers for their thorough review and constructive suggestions.

Reference (30)

Catalog

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return