Anthropogenic forcing has been identified as the dominant factor for the rapid SAT increase over China in recent decades (Allabakash and Lim, 2022; Sun et al., 2022) (Table 1). During the period 1961–2005, the SAT over China increased by 0.78°C ± 0.27°C, with warming of 0.25°C (10 yr)−1 and 0.17°C (10 yr)−1 resulting from GHG emissions and other anthropogenic factors, respectively (Allabakash and Lim, 2022). Compared to other parts of China, the TP has shown a greater warming rate in recent decades (1.23°C from 1961–2005) (Zhou and Zhang, 2021), and GHG forcing has contributed an increase of 1.37°C according to best estimates (Zhou and Zhang, 2021). GHG forcing was the major contributor to the increased warming, and AA induced a cooling effect and offset part of the warming resulting from the GHG effect over the whole of China and in a few subregions (Jia et al., 2020;; Zhou and Zhang, 2021) (Fig. 2). In addition to GHG and AA, LUCC and urbanization also influenced the local or regional SAT trends over China (Du et al., 2019; Jin et al., 2021; Yang et al., 2021; Zhang et al., 2022a) (Fig. 2). LUCC has generally induced significant SAT decreases in cropland areas and significant SAT increases in urbanized areas over China (Jin et al., 2021; Yang et al., 2021). Urbanization contributed to an increase of 0.49°C in the annual SAT over the whole of China from 1961 to 2013 (Sun et al., 2022). Urbanization also contributed to the diurnal temperature range trends by more than 25% in both winter and summer, and positively affected the surface sunshine duration by approximately 29.4% in winter and 11.9% in summer, over China from 1951 to 2020 (Zhang et al., 2022a). Moreover, the urban drying island effect in recent periods over eastern China, resulting from the decreased cloud cover, induced a mitigation effect on the urban heat island effect (Du et al., 2019).
Climatic/environmental factor Region (site) Period Season Quantitative contribution
Reference SAT trend Whole of China 1961–2013 Annual ↑0.49°C (urbanization) Sun et al. (2022) TP 1961–2005 Annual ↑1.37°C (GHG) Zhou and Zhang (2021) WC 1958–2015 Annual ↑ (GHG) Wang et al. (2018) WC (WBGT) 1961–2010 Summer ↑1.178°C (ANT) Li et al. (2020a) EC (WBGT) 1961–2010 Summer ↑0.708°C (ANT) Li et al. (2020a) Precipitation trend Whole of China (trend in extreme) 1961–1990 Annual ↑ (GHG); ↓ (AA); hard to separate from NAT Dong et al. (2022) Whole of China 1961–2012 Summer ↑ (GHG) Lu et al. (2020a) SEC 1961–2014 Summer ↑ (GHG); ↓ (AA) Guo et al. (2023) NWC 1961–2014 Summer ↑ (AA) Guo et al. (2023) NEC 1961–2014 Summer ↓ (ANT) Guo et al. (2023) SWC 1961–2014 Summer ↓ (AA) Guo et al. (2023) NSTP 1961–2013 Summer ↑ (ANT) Zhao et al. (2022) STP 1961–2013 Summer ↓ (ANT) Zhao et al. (2022) NC 1994–2011 Summer ↑ (GHG); ↓ (AA) Zhang et al. (2020a) SC 1994–2011 Summer ↑ (GHG) Zhang et al. (2020a) SWC 1993–2007 Autumn ↓ (AA) Huo et al. (2021) EC 1956–2003 Annual ↑ (GHG); ↓ (AA) Ma et al. (2017b) TCZ 1951–2005 Late summer ↓ (AA) Zhao et al. (2021) SC/Shenzhen (rural) 1979–2020 Annual ↑ (urbanization) Zhang et al. (2020d) CP-SFND 1961–2013 Annual ↑ (ANT) Zhou et al. (2021a) Drought trend Whole of China (flash drought) 1959–2005 Annual ↑77 ± 26% (GHG) Yuan et al. (2019) NC/YRB 1960–2010 Annual ↑ (reservoir operation) Omer et al. (2020) SEPTP 1959–2015 Autumn ↑ (anthropogenic warming) Ma et al. (2017a) WC/YlRB 1961–2017 Annual ↑ (reservoir impoundment) Liang et al. (2021) NC 1961–1015 Autumn ↑ (anthropogenic warming) Zhang et al. (2022e) SC 1961–1015 Autumn ↑ (rainfall change by anthropogenic
Zhang et al. (2022e) NC/BTH (VPD) 1951–2017 Annual ↑ ≥ 30% (urbanization) Wang et al. (2022a) EC/YRD (VPD) 1951–2017 Annual ↑ ≥ 30% (urbanization) Wang et al. (2022a) SC/PRD (VPD) 1951–2017 Annual ↑ ≥ 30% (urbanization) Wang et al. (2022a) WC/SCB (VPD) 1951–2017 Annual ↑ ≥ 30% (urbanization) Wang et al. (2022a) SWS Whole of China 1981–2021 Annual ↓ (GHG; LUCC) Zha et al. (2021) EC 1991–2015 Annual ↓ (LUCC) Li et al. (2018c) NWC 1980–2012 Annual ↓ (ANT) Zheng et al. (2018) NC/BTH 1980–2018 Annual ↓ 0.37 m s−1 yr−1 (urbanization) Wang et al. (2020a) CE SWC 1967–2010 Summer ↑ (ANT) Wu et al. (2022) NEC 1951–2014 Summer ↑ (ANT; GHG) Li et al. (2022b) EC 1961–2018 Summer ↑ (GHG); ↓ (urbanization) Yu and Zhai (2020) EC 1965–2014 Summer ↑ (GHG) Wang e al. (2022b)
Table 1. Quantitative contributions of anthropogenic forcings to regional climate changes over China obtained from references published since 2018. See Table 3 for abbreviation definitions.
Figure 2. The dominant anthropogenic factors influencing the trend of regional climate changes (including a few climatic parameters) over (a) the whole of China and (b) in a few subregions of China based on study results obtained from references published since 2018. The upward/downward histograms indicate uptrends/downtrends. For the quantitative contributions and further detailed information, please see Table 1. Panel (a) highlights the influences of different anthropogenic factors [legend provided in the lower-left corner of panel (b)] on different climate variables [legend provided in the upper-left corner of panel (b)] over the whole of China (i.e., the nationwide scale). Panel (b) highlights the influences of different anthropogenic factors [legend in the lower-left of panel (b)] on different climate variables [legend in the upper-left of panel (b)] over the subregions [legend in the upper-right of panel (b)].
Apart from the increased SAT, human activity has also contributed to a significant weakening of the SAT seasonality on the TP (Duan et al., 2017, 2019) and over China (Qian and Zhang, 2015). This mainly features a significant reduction in the winter–summer SAT difference, driven by the combined effect of increased GHGs and AAs with a meridional difference (Duan et al., 2019). In particular, anthropogenic forcing–induced SAT seasonality weakening has been tracked back to the 1870s on the TP (Duan et al., 2019). A detection and attribution study (Duan et al., 2019) using tree ring–based reconstructions of the summer–winter temperature difference indicates that the anthropogenic signal is detectable since the 1870s and can be supported by natural climate proxy evidence (Fig. 3).
Figure 3. The (a, b) detectable anthropogenic signal and (c) natural evidence for the influence of anthropogenic forcing on temperature seasonality in the TP region since the 1870s. ALL, ANT and NAT mean all forcing, anthropogenic forcing and natural forcing, respectively. Panels (a) and (b) are the scaling factors and corresponding 90% confidence intervals derived from one-signal and two-signal detection and attribution analysis, respectively. The data used in (a, b) are derived from Fig. 4 of Duan et al. (2019). “Tem difference” (grey line) in (c) means the summer minus winter temperature difference, and the pink line is its 50-year Gaussian smoothing. CO2 (thick black line) and sulfate (dark cyan) concentrations are increasing downwards. The black triangle and the pink arrow in (c) indicate the corresponding turning points. The data used in (c) are derived from Fig. 2 of Duan et al. (2019).
Anthropogenic forcing has influenced the intensity, frequency and occurrence likelihood of extreme temperature events in many regions of China (Chen et al., 2019a; Yin et al., 2019; Lu et al., 2020b; Liu et al., 2022). In view of the whole of China, both intensity and frequency indices of extreme temperature showed continuous warming from 1951 to 2018, and more intense and more frequent warm extremes and less intense and less frequent cold extremes were observed in most regions (Hu and Sun, 2022). GHG forcing accounted for approximately 1.6 (1.1 to 2) times the observed warming reflected in the changes in most indices, while AA offset approximately 35% (10%–60%) of GHG-induced warming for warm extremes, and land use and ozone may have made very small positive contributions to extreme temperatures (the effect of ozone was separated from the GHG effect) (Hu and Sun, 2022). Anthropogenic forcing is also a critical factor affecting the observed decadal changes in temperature extremes over China since the mid-1990s (Chen and Dong, 2019) and has contributed to the increased frequency, intensity, and spatial extent of regional daytime and nighttime heatwaves (HWs) over China in recent decades (Lu et al., 2018; Wang et al., 2018b; Yin and Sun, 2018; Su and Dong, 2019; Wang et al., 2022b). These changes were driven both directly by the strengthened greenhouse effect and indirectly by the related land‒atmosphere and circulation feedbacks (Su and Dong, 2019). Estimations have indicated that the increased probability of the hottest day occurring over more than 75% of the observed areas in China could be attributed to anthropogenic forcing (Chen et al., 2021), and the GHG effect changed the frequencies of summer days and tropical nights by +3.48 ± 1.45 d (10 yr)−1 and +2.99 ± 1.35 d (10 yr)−1 over eastern China from 1960 to 2012, respectively (Wang et al., 2018b). Nevertheless, local AA emissions may be a factor affecting the spatially heterogeneous extreme temperature trends in China (Chen and Dong, 2019).
Many case-based quantitative attribution studies have also demonstrated that anthropogenic forcing was the dominant factor affecting the increased frequency and intensity of extreme high-temperature events over China in recent decades (Fig. 4, Table 2). Anthropogenic forcing explained approximately 42% of the SAT warming and 60% (40%) of the increases in maximum (minimum) temperature, respectively, corresponding to extreme summer heat in western China in 2015 (Chen et al., 2019a). Simulation-based attribution results show that, given the external forcing at the 1961–2015 level and regardless of the sea surface boundary conditions, there is a 21-fold increase in the likelihood of 2015-like heat events in Northwest China due to anthropogenic forcing (Zhang et al., 2022d). Anthropogenic forcing has made the occurrence likelihood of July 2017–like HW events over eastern China increase by a factor of 4.8 (Sparrow et al., 2018) and the frequency of July 2017–like HWs in central eastern China become approximately 1 in 5 years under current climate conditions (Chen et al., 2019b). Due to the combined influences of anthropogenically forced warming (~78%) and urbanization (~17%), July and August 2018–like HW events in Northeast China have become a one-in-60-year event in urban regions and a one-in-80-year event in rural regions (Zhou et al., 2020). Simultaneously, anthropogenic warming has made 30-day persistent nighttime HWs like the event of summer 2018 in Northeast China become about a one-in-60-year event (compared to about a one-in-a-500-year event naturally) (Ren et al., 2020). Moreover, anthropogenic forcing has also contributed to extreme warm events in spring, such as the 2018 event that occurred over eastern China, and has increased the chance of this event occurring by tenfold (Lu et al., 2020b). Aside from GHG-induced warming, urbanization, the effects of energy consumption (i.e., AHR) and urban development have also contributed to hot extremes over China (Chen and Dong, 2019; Sun et al., 2019; Yang et al., 2019, 2021; Liu et al., 2021; Wang et al., 2021a). Urbanization has contributed about 30%–40% of the nighttime high temperature extremes since 1960 over eastern China (Sun et al., 2019). AHR can drastically aggravate urban heat stress (Yang et al., 2021), and has contributed an annual increase of 0.02–0.19 days of extreme heat events in Beijing city center (Liu et al., 2021). It was revealed that urban development increased the total thermal discomfort hours by 27% in the urban areas of the Yangtze River Delta during the period 2009–2013, with AHR and urban land use contributing nearly equal amounts (Yang et al., 2019). The mean contributions of urbanization to the maximum daily maximum temperature, high-temperature days, and hot-night days were 68%, 45% and 27%, respectively, in Beijing, Tianjin and Shijiazhuang (Wang et al., 2021c).
Figure 4. Anthropogenic forcing–induced changes in the occurrence likelihoods of record-breaking climate/weather events that occurred in China in recent years from study results published since 2018. The marker shape in the top-left denotes the type of event, and the open markers in the middle-left indicate detectable anthropogenic signals without quantitative estimations. The solid markers in the bottom-left, upper-right and middle-right indicate detectable anthropogenic signals with quantitative estimations of increased intensity, increased probability, and decreased probability, respectively. The different colors denote the different events listed in Table 2. The color bar shows 24 events corresponding to the event numbers listed in Table 2.
Number Extreme event(s) Region (site) Duration Quantitative contribution
Reference 1 extreme low sunshine YRD Jan–Feb 2019 ↑ 3.1 times; ↑ 1.3 times (probability) (AA; GHG) He et al. (2021) 2 extreme low temperature TP Feb–Mar 2019 ↓ 80% (probability) (human activity) Duan et al. (2021) 3 extreme low temperature NEC 19–25 Apr 2022 ↓ 80% (probability) (human activity) Yu et al. (2022) 4 extreme low temperature NC 21−25 Jan 2016 ↓ 89% (probability) (human activity) Sun et al. (2018) 5 extreme low temperature EC 6–8 Jan 2021 detectable (human activity) Liu et al. (2022) 6 extreme low precipitation SWC Apr–Jun 2019 ↑ ~6 times (probability) (human activity) Lu et al. (2021) 7 extreme drought NC/Beijing Winter 2017 ↑ 1.29 times (probability) (human activity) Du et al. (2020) 8 extreme compound events SC Apr–May 2018 ↑ 17 times (probability) (human activity) Zhang et al. (2020b) 9 extreme compound events SWC/Yunnan Spring–early summer 2019 ↑ 43% (probability) (human activity) Wang et al. (2021b) 10 extreme compound events SWC May–Jun 2019 ↑ 7.21 times (probability) (anthropogenic warming) Du et al. (2021) 11 extreme compound events
SC Summer 2020 ↑ 3.51 times (probability) (anthropogenic warming) Du et al. (2022) 12 extreme high temperature
NC/SEC Winter 2016 ↓ detectable (water vapor/ aerosols) Zhang et al. (2022b) 13 extreme high temperature EC/Shanghai Jul 2017 ↑ ≥10 times (probability) (anthropogenic warming) Chen et al. (2019b) 14 extreme high temperature NEC Jul–Aug 2018 ↑ 78% (probability) (anthropogenic) Zhou et al. (2020) 15 extreme high temperature CEC 21–25 Jul 2017 ↑ 4.8 times (probability) (human activity) Sparrow et al. (2018) 16 extreme high temperature EC Spring 2018 ↑ 10 times (probability) (human activity) Lu et al. (2020b) 17 extreme high temperature WC Summer 2015 ↑ 42% (intensity) (GHG) Chen and Dong (2018) 18 extreme high temperature SC Sep 2021 ↑ 50 times (probability) (anthropogenic
Wang and Sun (2022) 19 extreme high precipitation EC/Yangtze River Dec 2018–Feb 2019 ↓ 19% (intensity) (anthropogenic warming) Hu et al. (2021) 20 extreme high precipitation SC/Guangdong 14–16 Dec 2013 ↑ detectable (AA) Liu et al. (2021) 21 extreme high precipitation SWC/Sichuan 11–20 Aug 2020 ↑ 2 times (probability) (human activity) Qian et al. (2022) 22 extreme high precipitation EC Summer 2020 ↓ 46% (probability) (human activity) Zhou et al. (2021b) 23 extreme high precipitation NC/Beijing Winter 2020 ↑ 52.9% (probability) (human activity) Pei et al. (2022) 24 extreme high precipitation YRB June, July 2020 ↓ 54% (probability) (human activity) Lu et al. (2022)
Table 2. Quantitative contributions of anthropogenic forcings to regional record-breaking climate/weather events over China obtained from references published since 2018. See Table 3 for abbreviation definitions.
Simultaneously, anthropogenic forcing has significantly decreased regional extreme low-temperature events over China (Table 2). Although the reduced trend of cold events in southeastern China cannot be attributed with high confidence to any anthropogenic signal alone (Freychet et al., 2021), human influence can clearly be detected in the changes in cold spell durations (Lu et al., 2018), icy days, and frosty nights at the whole-China scale (Wang et al., 2018b; Yin and Sun, 2018). Due to anthropogenic-induced warming, the likelihoods of 2019-like early spring cold events occurring over the southeastern TP (Duan et al., 2021) and of April 2020–like cold events occurring over Northeast China (Yu et al., 2022) have reduced by about 80%. For the record-breaking three-day cold events since 1961 that occurred in January 2021 across eastern China, human activities have reduced the likelihood of such events by about 50% (Liu et al., 2020). Moreover, anthropogenically induced warming also partly contributed to the rapid switches between warm and cold extremes in winter (volatile winters) in China, leading to increased volatile winters in Northeast, Northwest, Southwest, Southeast China, and the Yangtze River Valley after 1980 (Chen et al., 2019c).
Based on the evidence obtained from studies published since 2018, some achievements are summarized as follows. Human activities, including GHG and AA emissions, urbanization, LUCC, and AHR, have contributed significantly to the SAT trend and extreme changes over China, and the anthropogenic signal can be tracked back to the 1870s in the TP region. The anthropogenic signal affecting precipitation over China is relatively obscure and can be dependent on the period, season, event intensity, spatial scope, or even model simulation. Anthropogenic forcing has also weakened monsoon precipitation, triggered regional droughts, and contributed to the decline in SWS over China. Moreover, anthropogenic forcing, especially GHG-induced warming, has intensified regional compound extreme climate/weather events (e.g., compound dry and hot events and compound daytime and nighttime HWs) over China in recent decades.
While the achievements mentioned above constitute great advancements, further studies have also covered the following points that, whilst challenging, should be considered in future research:
(1) In addition to the significant contribution of anthropogenic forcing to regional climate changes over China, decadal- and multidecadal-scale climate fluctuations may also contribute to those changes simultaneously. Quantitatively attributing regional climate changes to anthropogenic forcing and decadal or multidecadal climate fluctuations is still challenging because sometimes superimposed or counteracting effects occur between these factors.
(2) Many attribution studies have provided quantitative, confirming, and distinct evidence that anthropogenic forcing has influenced the regional climate over China in recent decades, while evidence for earlier periods is relatively rare due to the limited length of observational records. In view of the multitude of climate proxy data available over the TP and eastern China, such as tree rings, ice cores, and historical documents, the detection and attribution of anthropogenic signals based on the combined use of observations, climate proxy data and model simulations could provide further understanding or confirmational evidence regarding the influences of human activity on regional climate changes over China.
(3) To achieve the target of carbon neutrality, clean energy (e.g., hydropower) might be utilized more widely in the future, and this would induce very strong local human activities (e.g., to develop hydropower by building large reservoirs). Specifically, large dams (e.g., the Three Gorges Dam) have significantly influenced the regional climate (e.g., SAT, precipitation and moisture) and atmospheric circulation (Li et al., 2019; 2021a, 2021d, Chen et al., 2022). Apart from hydropower, solar energy is also a promising application (Yang et al., 2018). Solar energy exerts a significant influence, especially through changes in LUCC, on local climate (e.g., surface temperature and albedo) (Hua et al., 2022; Xia et al., 2022; Yang et al., 2022). Such strong local human activity certainly induces a great effect on the regional climate. Assessing and quantifying the influence of human activities on regional climate conditions is a popular and challenging scientific issue.
(4) Compared to general extreme climate/weather events, studies on the influence of human activity on compound extreme climate/weather events are still scarce. China, with the largest population worldwide, is highly vulnerable to meteorological disasters under climate change (IPCC AR6). In view of the great effect of compound extreme climate/weather events on human health/life and crops/plants (Wang et al., 2021a; Zhang et al., 2022c), further studies on the effects of human activity in terms of compound extreme climate/weather events might be a key scientific issue in the future.
Acknowledgements. This work was supported by the National Natural Science Foundation of China (Grant No. 41875113).
Abbreviation Full name Abbreviation Full name BTH Beijing–Tianjin–Hebei SEC Southeastern China CE Compound Events SEPTP Southeastern Periphery of the Tibetan Plateau CP/SFND circulation patterns /southern flood–northern drought STP Southern Tibetan Plateau EC Eastern China SWC Southwestern China NC Northern China TCZ transitional climate zone NEC Northeastern China TP Tibetan Plateau N/STP North/South Tibetan Plateau VPD Vapor Deficit NWC Northwestern China WBGT Wet Bulb Globe Temperature Index PRD Pearl River Delta WC Western China SAT Surface Air Temperature YlRB Yalong River Basin SC Southern China YeRB Yellow River Basin SCB Sichuan Basin YRD Yangtze river delta
Table 3. Abbreviations used in Tables 1 and 2.
|Climatic/environmental factor||Region (site)||Period||Season||Quantitative contribution |
|SAT trend||Whole of China||1961–2013||Annual||↑0.49°C (urbanization)||Sun et al. (2022)|
|TP||1961–2005||Annual||↑1.37°C (GHG)||Zhou and Zhang (2021)|
|WC||1958–2015||Annual||↑ (GHG)||Wang et al. (2018)|
|WC (WBGT)||1961–2010||Summer||↑1.178°C (ANT)||Li et al. (2020a)|
|EC (WBGT)||1961–2010||Summer||↑0.708°C (ANT)||Li et al. (2020a)|
|Precipitation trend||Whole of China (trend in extreme)||1961–1990||Annual||↑ (GHG); ↓ (AA); hard to separate from NAT||Dong et al. (2022)|
|Whole of China||1961–2012||Summer||↑ (GHG)||Lu et al. (2020a)|
|SEC||1961–2014||Summer||↑ (GHG); ↓ (AA)||Guo et al. (2023)|
|NWC||1961–2014||Summer||↑ (AA)||Guo et al. (2023)|
|NEC||1961–2014||Summer||↓ (ANT)||Guo et al. (2023)|
|SWC||1961–2014||Summer||↓ (AA)||Guo et al. (2023)|
|NSTP||1961–2013||Summer||↑ (ANT)||Zhao et al. (2022)|
|STP||1961–2013||Summer||↓ (ANT)||Zhao et al. (2022)|
|NC||1994–2011||Summer||↑ (GHG); ↓ (AA)||Zhang et al. (2020a)|
|SC||1994–2011||Summer||↑ (GHG)||Zhang et al. (2020a)|
|SWC||1993–2007||Autumn||↓ (AA)||Huo et al. (2021)|
|EC||1956–2003||Annual||↑ (GHG); ↓ (AA)||Ma et al. (2017b)|
|TCZ||1951–2005||Late summer||↓ (AA)||Zhao et al. (2021)|
|SC/Shenzhen (rural)||1979–2020||Annual||↑ (urbanization)||Zhang et al. (2020d)|
|CP-SFND||1961–2013||Annual||↑ (ANT)||Zhou et al. (2021a)|
|Drought trend||Whole of China (flash drought)||1959–2005||Annual||↑77 ± 26% (GHG)||Yuan et al. (2019)|
|NC/YRB||1960–2010||Annual||↑ (reservoir operation)||Omer et al. (2020)|
|SEPTP||1959–2015||Autumn||↑ (anthropogenic warming)||Ma et al. (2017a)|
|WC/YlRB||1961–2017||Annual||↑ (reservoir impoundment)||Liang et al. (2021)|
|NC||1961–1015||Autumn||↑ (anthropogenic warming)||Zhang et al. (2022e)|
|SC||1961–1015||Autumn||↑ (rainfall change by anthropogenic|
|Zhang et al. (2022e)|
|NC/BTH (VPD)||1951–2017||Annual||↑ ≥ 30% (urbanization)||Wang et al. (2022a)|
|EC/YRD (VPD)||1951–2017||Annual||↑ ≥ 30% (urbanization)||Wang et al. (2022a)|
|SC/PRD (VPD)||1951–2017||Annual||↑ ≥ 30% (urbanization)||Wang et al. (2022a)|
|WC/SCB (VPD)||1951–2017||Annual||↑ ≥ 30% (urbanization)||Wang et al. (2022a)|
|SWS||Whole of China||1981–2021||Annual||↓ (GHG; LUCC)||Zha et al. (2021)|
|EC||1991–2015||Annual||↓ (LUCC)||Li et al. (2018c)|
|NWC||1980–2012||Annual||↓ (ANT)||Zheng et al. (2018)|
|NC/BTH||1980–2018||Annual||↓ 0.37 m s−1 yr−1 (urbanization)||Wang et al. (2020a)|
|CE||SWC||1967–2010||Summer||↑ (ANT)||Wu et al. (2022)|
|NEC||1951–2014||Summer||↑ (ANT; GHG)||Li et al. (2022b)|
|EC||1961–2018||Summer||↑ (GHG); ↓ (urbanization)||Yu and Zhai (2020)|
|EC||1965–2014||Summer||↑ (GHG)||Wang e al. (2022b)|