The 2022 Extreme Heatwave in Shanghai, Lower Reaches of the Yangtze River Valley: Combined Influences of Multiscale Variabilities

Ping LIANG, Zhiqi ZHANG, Yihui DING, Zeng-Zhen HU, and Qi CHEN

1Key Laboratory of Cities’ Mitigation and Adaptation to Climate Change in Shanghai, Shanghai Regional Climate Center,China Meteorological Administration, Shanghai 200030, China

2National Climate Center, China Meteorological Administration, Beijing 100081, China

3Climate Prediction Center, NCEP/NWS/NOAA, College Park, MD 20740, USA

ABSTRACT In the summer of 2022, China (especially the Yangtze River Valley, YRV) suffered its strongest heatwave (HW) event since 1961.In this study, we examined the influences of multiscale variabilities on the 2022 extreme HW in the lower reaches of the YRV, focusing on the city of Shanghai.We found that about 1/3 of the 2022 HW days in Shanghai can be attributed to the long-term warming trend of global warming.During mid-summer of 2022, an enhanced western Pacific subtropical high (WPSH) and anomalous double blockings over the Ural Mountains and Sea of Okhotsk, respectively, were associated with the persistently anomalous high pressure over the YRV, leading to the extreme HW.The Pacific Decadal Oscillation played a major role in the anomalous blocking pattern associated with the HW at the decadal time scale.Also,the positive phase of the Atlantic Multidecadal Oscillation may have contributed to regulating the formation of the doubleblocking pattern.Anomalous warming of both the warm pool of the western Pacific and tropical North Atlantic at the interannual time scale may also have favored the persistency of the double blocking and the anomalously strong WPSH.At the subseasonal time scale, the anomalously frequent phases 2-5 of the canonical northward propagating variability of boreal summer intraseasonal oscillation associated with the anomalous propagation of a weak Madden-Julian Oscillation suppressed the convection over the YRV and also contributed to the HW.Therefore, the 2022 extreme HW originated from multiscale forcing including both the climate warming trend and air-sea interaction at multiple time scales.

Key words: extreme heatwave, multiscale variability, air-sea interaction, warming trend, Yangtze River Valley, Shanghai Citation: Liang, P., Z.Q.Zhang, Y.H.Ding, Z.-Z.Hu, and Q.Chen, 2024: The 2022 extreme heatwave in Shanghai,lower reaches of the Yangtze River Valley: Combined influences of multiscale variabilities.Adv.Atmos.Sci., 41(4),593-607, https://doi.org/10.1007/s00376-023-3007-8.

1.Introduction

Heatwaves (HWs) usually exert significant influences on highly populated East Asia, especially the middle and lower reaches of the Yangtze River Valley (YRV) (e.g., Tan et al., 2007; Ding et al., 2010; Chen and Li, 2017; Hu et al.,2017; Hu and Huang, 2020).From mid-June through mid-August 2022, an extreme HW event affected China, centering on the YRV.Considering the intensity, coverage, and duration, this was the most severe HW event since comprehensive meteorological observations started to be recorded in China in 1961 (http://finance.people.com.cn/n1/2022/0817/c1004-32504584.html).It caused problems with energy consumption, excessive fatalities, and wildfires.Understanding the causes of such an extreme HW event will be of benefit to our understanding and forecasting of HWs, as well as grasping their impacts on aspects such as human health, energy consumption, and water supply, etc.

Previous studies have investigated the causes of some other extreme HWs.However, the atmospheric circulation and ocean anomalies directly associated with extreme HWs vary from case to case and are regionally dependent (Chen and Lu, 2015).In the YRV, HWs are associated with atmospheric internal variability, oceanic anomalous forcing, and climate change effects.In terms of the atmospheric factors,a westward-expanded and enhanced western Pacific subtropical high (WPSH) is a crucial factor for HWs in the YRV(Chen and Lu, 2015; Wang et al., 2016, 2017; Chen et al.,2019; Hu and Huang, 2020).Besides a low-level warm air anomaly corresponding to the equivalent barotropic geopotential height anomaly associated with a stronger WPSH based on the basic hydrostatic balance relationship, WPSHassociated atmospheric anticyclonic activity can induce more diabatic heating at the surface by reducing cloud cover and increasing solar radiation, thus favoring the occurrence of HWs (Dole et al., 2011; Qian et al., 2016; Qian, 2017; Li et al., 2023; Zhang et al., 2023).In addition to the influence of the WPSH, HWs are also associated with atmospheric circulation anomalies over middle and high latitudes (Zheng and Wang, 2019).Kim et al.(2022) attributed the interannual variability of East Asian HWs to the circumglobal teleconnection and the Arctic-Siberian Plain teleconnection patterns.A poleward-displaced East Asian jet stream and westwardexpanded and enhanced WPSH system caused the East China HW of 2013 (Wang et al., 2016).Deng et al.(2019)proposed that YRV HWs are linked to the North Atlantic Oscillation, which may trigger a southeastward-propagating wave train over northern Russia and East Asia and further result in a high-pressure anomaly over the YRV.

In terms of the boundary forcing, sea surface temperature anomalies (SSTAs) in some ocean basins may be associated with YRV HWs.However, the critical regions of SSTAs and the physical processes involved vary in different studies.Luo and Lau (2019) suggested that El Niño (La Niña) significantly amplifies (weakens) the HW activities in most areas of China, especially in southern China.However, Chen et al.(2018) argued that the long-lived HWs in southern China are associated with the transition phase from El Niño to La Niña.Wang et al.(2017) attributed major variability modes of HWs in China to western Pacific SSTAs.Chen et al.(2019) noted that the extremely hot mid-summer in Central and South China during 2017 was connected with the warming of the western tropical Pacific rather than El Niño-Southern Oscillation.Meanwhile, warming in the Indian Ocean may also be connected with a westward-expanded WPSH(Hu et al., 2012; Ding et al., 2021).In addition, the summer tripolar SSTA pattern over the North Atlantic can amplify the propagation of Rossby wave energy to East Asia, resulting in HWs over the Yangtze-Huaihe River Basin in China(Deng et al., 2019).Thus, it can be seen that the key SST regions associated with extreme HWs in the YRV are still a controversial topic.

HWs during recent decades in East Asia, including the YRV, have also been partially attributed to the global warming trend (e.g., Sun et al., 2014; Kim et al., 2022; Liang et al., 2022; Rogers et al., 2022).For instance, Sun et al.(2014) theoretically argued that anthropogenic influence has caused a more than a 60-fold increase in the likelihood of the extremely hot summer in 2013 since the early 1950s.Rogers et al.(2022) indicated that the increasing occurrence of HWs is mainly driven by warming baseline temperatures due to global warming, but changes in weather patterns contribute significantly to disproportionate increases over parts of Europe, the eastern United States, and Asia.

Thus, it has been concluded that the causes of extreme HWs are complicated and still not fully understood.The causes of YRV HWs and their associated mechanism is still a debated topic.In the present study, by taking Shanghai as an example because of its longest continuous record of daily surface air temperature (SAT) in China, we investigated the possible mechanism that induced China’s 2022 extreme HW event by focusing on the frequency of HWs in the YRV with 2-m maximum SAT (Tmax,2m) greater than or equal to 35°C from the viewpoint of variabilities at multiple temporal scales from subseasonal to long-term change.A brief description of the data and analysis methods used in this study is given in the next section.The anomalies of the 2022 extreme HW and the associated circulation background are presented in section 3, and the influences of climate change, interannual SST forcing of three ocean basins(Pacific, Atlantic, and Indian), and subseasonal variabilities are discussed in sections 4-6, respectively.The final section summarizes our main findings and provides some further discussion.

2.Data and methods

2.1.Data

Continuous daily SAT observations at Xujiahui (XJH)from 1 December 1872 to 30 September 2022 were collected and quality-controlled by the Shanghai Meteorological Information Centre, Shanghai Meteorological Bureau, and then reprocessed by Liang et al.(2022) to improve the consistency by eliminating some biases due to the observation site, methods, and changes in instrumentation.Daily gridded SAT reanalysis data with a spatial resolution of 0.5° × 0.5° from January 1979 to August 2022 were provided by the CRA-40/Land dataset developed by the National Meteorological Information Center of the China Meteorological Administration (http://data.cma.cn/analysis/cra40).CRA-40/Land is China’s first generation global land surface reanalysis.Compared with other global land reanalysis datasets, CRA-40/Land assimilates abundant extra ground station observations and multiple satellite remote sensing data to improve the quality of its near-surface atmospheric forcing data, and therefore provides high-quality near-surface and surface variables,especially in East Asian regions (Liang et al., 2020; Yang et al., 2021).The HadCRUT5 global temperature dataset(Morice et al., 2021; https://crudata.uea.ac.uk/cru/data/temperature/) was used to provide the global background of long-term warming.

Daily atmospheric data with a 2.5° × 2.5° horizontal resolution from 1979 to 2022 were acquired from the National Centers for Environmental Prediction-National Center for Atmospheric Research Reanalysis (Kalnay et al., 1996)(https://psl.noaa.gov/data/gridded/data.ncep.reanalysis.html).Daily outgoing longwave radiation (OLR) on a 2.5° × 2.5°horizontal resolution from the National Oceanic and Atmospheric Administration (Liebmann and Smith, 1996) (https://www.ncei.noaa.gov/products/climate-data-records/outgoinglongwave-radiation-daily) was used as a proxy for atmospheric convection.Daily real-time multivariate Madden-Julian Oscillation (MJO) indices (RMM1 and RMM2) downloaded from http://www.bom.gov.au/climate/mjo/ were used for defining the phases of the MJO (Wheeler and Hendon, 2004).Daily boreal summer intraseasonal oscillation(BSISO, Lee et al., 2013) indices (BSISO1 and BSISO2)were acquired from https://www.apcc21.org/ser/moni.do?lang=en for determining the phases of BSISO.

Monthly mean SST data with a 2° × 2° horizontal resolution from 1979 to 2022 were obtained from the Extended Reconstructed SST, version 5 ( Huang et al., 2017) (https://climatedataguide.ucar.edu/climate-data/sst-data-noaaextended-reconstruction-ssts-version-5-ersstv5).Monthly indices of the Pacific Decadal Oscillation (PDO; Mantua et al., 1997) from 1900 to 2022 (https://psl.noaa.gov/gcos_wgsp/Timeseries/PDO/) and Atlantic Multidecadal Oscillation (AMO; Enfield et al., 2001) from 1873 to 2022(http://www.psl.noaa.gov/data/timeseries/AMO/) were used to examine the impacts of these two multidecadal variability modes on the HW in summer 2022.A set of monthly climate system monitoring indices was acquired from the National Climate Center of China, the definitions of which are provided in Table 1.

2.2.Statistical analysis methods

Ensemble empirical mode decomposition (EEMD; Wu and Huang, 2009) was used to separate the time series of SAT and HW days into different time scales including interannual, decadal-interdecadal variabilities as well as long-term trends.The EEMD calculations included four steps: (1) a noise series was added to the target data; (2) the data with the added noise were decomposed into intrinsic mode functions (IMFs); (3) steps (1) and (2) were repeated with different noise series each time; and (4) the final results were acquired as the ensemble means of the corresponding IMFs of the decompositions.

For the daily geopotential height at 500 hPa (H500) and OLR, the Lanczos filter (Duchon, 1979) was adopted to obtain the intraseasonal component during the summer of 2022.

3.Extreme HW and associated atmosphere circulation

China experienced a persistent HW in the summer of 2022.As shown in Fig.1a, by taking 1991-2020 as the base time, the average SAT anomaly was higher than 2°C over the YRV from July to August of 2022.For example, the SAT anomaly (2.5°C) was the highest at XJH in Shanghai since records began 1873.The SAT anomaly at XJH is highly consistent with those over the YRV, especially the lower reaches of the YRV, with correlation coefficients above 0.75 (Fig.1b).Therefore, XJH, with its long and continuous set of daily observations since December 1872, has a statistically coherent variation with that over the lower reaches of the YRV.As shown in Fig.1c, both the daily Tmax,2mand 2-m minimum SAT (Tmin,2m) in JJA 2022 were steadily larger than their corresponding climatologies in 1991-2020 at XJH.Specifically, there were 48 discomfort days with both daily Tmax,2m≥ 35°C and daily Tmin,2m≥ 25°C during the summer of 2022, which broke the previous record of 46 days in 2013.

The atmospheric circulation and its anomalies during JJA 2022 (Figs.2a and b) show that the WPSH was abnormally strong and sufficiently westward-expanded to connect with the Iran high.The anomalous high-pressure anticyclone sustained the HW over the YRV via positive feedback among subsidence airflow, cloud reduction, and enhanced downward solar radiation (Li et al., 2023).Meanwhile, the combined influences of a persistent anomalous double-blocking pattern over the Ural Mountains and the Sea of Okhotsk and the stable circumglobal high-pressure belt over the Eurasian continent were not favorable for the intrusion of cold air from the Kara Sea to East China.As Fig.2c shows,consistent with the pressure gradient force induced by the double-blocking pattern over the Ural Mountains and the Sea ofOkhotsk, the anomalous cold air from the Kara Sea was transported to Northeast China by the enhanced westerlies over the region south of Lake Baikal, and southerly winds prevailed over East China.Further analysis (figure omitted)showed that the double-blocking pattern was the strongest it has been since 1979.The strength of the WPSH, meanwhile,ranked second only to that in 2010, while its extent of westward expansion ranked third behind that in 2010 and 2016,according to the location of its westernmost point.Thus, the extreme HW in the mid-summer of 2022 was caused by the persistent large-scale atmospheric circulation, including both the strong and westward-expanded WPSH and the persistent double-blocking pattern over the Eurasian continent.

Table 1.SST indices and their values in June-August (JJA) 2022.

Fig.1.Spatial distribution of the (a) SAT anomaly in East Asia during JJA 2022 (units: °C) and (b) correlation coefficients of SAT in East Asia with SAT at XJH in Shanghai (dotted areas denoting statistical significance at the greater than 0.95 confidence level).(c) The daily maximum/minimum SAT evolution at XJH in the summer of 2022(units: °C; “2022 max”/“2022 min” and “1991-2020 max”/“1991-2020 min” represent the maximum/minimum SAT in 2022 and the 1991-2020 average, respectively).

4.Influence of climate change

East Asia is a region showing a significant warming trend, which may be largely due to the increases in greenhouse gas concentrations associated with human activities (e.g.,Hu et al., 2003; Sun et al., 2014; Liu et al., 2019; IPCC,2021).Human-induced warming has led to an increase in the frequency and intensity of temperature means and extremes (Bindoff et al., 2014; Liu et al., 2019; IPCC, 2021).But what was the contribution of the long-term warming trend to the 2022 record-breaking HW in China? To answer this, EEMD was applied to isolate the long-term trend component of global SAT and HW days (Tmax,2m≥ 35°C) at XJH during summer (June-September) (Fig.3a).The trend components of HW days and SAT at XJH were largely consistent and emerged since the beginning of the 20th century.The frequencies of HW events (Tmax,2m≥ 35°C) increased dramatically when the SAT started to increase significantly from around the mid-1980s (Fig.3b).Specifically, the occurrence frequency percentage of summer HWs at XJH increased from 5.6% to 15.5% from the mid-1980s to the beginning of the 2020s.Thereinto, for HWs with Tmax,2m≥ 37°C, the occurrence frequency percentage (5.4%) from the 1990s to the present increased significantly, by about a half, in comparison with the earlier period from the 1930s to 1950s (3.4%), suggesting that extremely hot weather is more likely to occur in the present global warming background.

Fig.2.The (a) H500 and (b) its anomalies (units: gpm), as well as (c) the anomalous vector winds at 500 hPa,during JJA 2022 (units: m s-1).Solid black contours and dashed red contours in (b) represent 5880 gpm of H500 in JJA 2022 and the 1991-2020 climatology, respectively.

Similar to in Seneviratne et al.(2016), the quantitative contribution of the long-term warming to the 2022 extreme HW was assessed by Ct, the Ct(units: %) calculated as follows:

whereNuthrepresents the variation in HW days with a global SAT change of 1°C, and dNoh(dTt) denotes the observational HW days anomaly (long-term trend component of the global SAT anomaly) in 2022 in comparison with the corresponding averages in 1956-1985.The reason for choosing 1956-1985 as the base period was that HWs increased distinctively after the mid-1980s in both Shanghai (Fig.3b) and eastern China (Sun et al., 2014).Based on the global SAT data,HW days increase by about 4.55 days in Shanghai when the global temperature increases by 0.1°C, i.e.Nuth= 45.5.In comparison with 1956-1985, thedNohanddTtwere 39.8 days and 0.29°C, respectively, during the summer of 2022.Thus, about 1/3 of the HW days can be attributed to the long-term trend component of the global SAT anomaly,which is similar to the result (30%-40%) for eastern China(Jiang et al., 2023; Li et al., 2023).In other words, the longterm warming can increase both the occurrence risk and frequency of extreme HWs in the lower reaches of the YRV.

5.Combined influence of oceanic anomalies

5.1.Combined influence of the AMO and PDO

A positive phase of the AMO and a negative phase of the PDO, as an important background at the interdecadal timescale, concurrently occurred in the summer of 2022(not shown).But how did these oceanic variabilities affect the 2022 extreme HW? By compositing 17 mid-summers(JJA) with a positive phase of the AMO and a negative phase of the PDO since the end of the 1990s (1998, 1999,2000, 2001, 2005, 2006, 2008, 2009, 2010, 2011, 2012,2013, 2017, 2018, 2020 and 2021), the joint influence of the positive phase of the AMO and negative phase of the PDO on H500 during mid-summer is shown in Fig.4.Here, 11-year running averages of the PDO and AMO indexes were used to acquire the years with positive AMO and negative PDO.The composite anomalous circulation was tested based on the calculated effective degrees of freedom by taking serial correlation into account (Zwiers and von Storch,1995).A zonally orientated wave train teleconnection appears across the Eurasian mid-to-high latitudes from the North Atlantic to northern East Asia along the Asian westerly jet waveguide route.H500 anomalies are characterized by a double-blocking pattern over Eurasian mid-to-high latitudes,which is similar to the anomalous circulation during the midsummer of 2022 (Fig.2b).Considering that the geopotential height anomalies over the Sea of Okhotsk are statistically significant, the PDO plays a more major role in the anomalous blocking pattern at the decadal time scale.Also, the positive phase of the AMO may also contribute to regulating the formation of the double-blocking pattern.

Fig.3.(a) Long-term trend components of SAT and HW days (Tmax,2m ≥ 35°C) at XJH during summer since 1873 (units: °C); and (b) the occurrence probabilities of HWs (orange curve and red curve denote HWs with Tmax,2 m ≥ 35°C and Tmax,2 m ≥ 37°C, respectively) since 1985 (%).

Fig.4.Composite H500 anomaly (units: gpm) during summer under a positive phase of the AMO and negative phase of the PDO.Hatching denotes statistical significance at the greater than 90% confidence level.

From the interdecadal component of the mid-summer SAT at XJH compared with the AMO and PDO (Fig.5), the three warm phases of the interdecadal variability of SAT, i.e., from the late 1870s to the late 1890s, from the mid-1940s to the early 1960s, and from the late 1990s to the present, were consistent with the combined interdecadal phase of the AMO and negative phase of the PDO.For HW days, the result (figure omitted) was similar, especially since the late 1990s.The negative correlation between the SAT and the PDO index is stably significant above the 95%confidence level since the 1870s, further suggesting a major role played by the negative phases of the PDO in the warm SAT at the decadal timescale.The relationship between the SAT and AMO before the 1960s was weaker than that after the 1960s.Considering that the data history is a little short for the composite analysis of the AMO at multidecadal time scales, the impacts of the AMO on HWs need to be further verified in future work.

5.2.Possible influences of interannual anomalies of three tropical oceans

The global SSTAs during JJA 2022 are shown in Fig.6.Over the tropical oceans, distinct warm anomalies covered the Pacific warm pool and the tropical North Atlantic Ocean.In particular, the SSTA was above 1°C over some areas of the Pacific warm pool, which is associated with the tripledip La Niña over the tropical central and eastern Pacific (Li et al., 2022).Meanwhile, the SST over the Indian Ocean Basin cooled during the mid-summer of 2022.

Fig.5.The decadal-multidecadal component of average SAT (red curve) in comparison with the 11-year running averaged AMO (green curve) and PDO (black curve) indices during JJA since the 1870s (units: °C;gray shading denotes periods with positive-phase AMO and negative-phase PDO).

Fig.6.SSTAs during JJA 2022 (units: °C).

To examine the connections of the three tropical oceans with the 2022 extreme HW, Figs.7a-c display the simultaneous regression of H500 onto the three SSTA indices, i.e.,WPWPS, TNA, and IOBM, during JJA in 2000-2021.The spatial correlation coefficients between the observed anomalies over the Eurasian continent (10°-80°N, 30°-180°E)in 2022 and those regressed onto the detrended TNA and IOBM indices are 0.70 and 0.59, respectively, with both above the 0.05 significance level.The spatial correlations between the observed anomalies over the East Asian subtropical region (10°-40°N, 90°-150°E) in 2022 and those regressed onto the detrended WPWPS index is 0.65, above the 0.05 significance level.Therefore, the anomalous circulation patterns associated with the detrended WPWPS, TNA,and IOBM indices are similar to the observations in 2022.Specifically, positive regressions over the western Pacific-East China together with the observed positive WPWPS and TNA indices in the summer of 2022 (Table 1) suggest a possible impact of the warming in the tropical western Pacific and the tropical Atlantic Ocean on the enhancement of the WPSH during mid-summer of 2022.

Fig.7.Simultaneous regression of H500 (shading; units: gpm) onto the detrended (a) WPWPS,(b) TNA, and (c) IOBM indices during JJA 2000-2021 and (d) the reconstructed H500 anomaly by using the indices of the three tropical oceans in 2022.Hatching denotes statistical significance at the greater than 95% confidence level.

For the Eurasian mid-to-high latitudes, the observed positive H500 anomalies in summer 2022 are similar to the regression pattern with the TNA index and the regression pattern with the opposite sign with the IOBM index.This implies that the observed H500 anomalies over the Ural Mountains and the Sea of Okhotsk may be partially associated with the positive TNA index and negative IOBM index in summer 2022 (Table 1).Thus, the joint influences of the tropical Atlantic and Indian Ocean basins may have cooperatively contributed to the double-blocking anomaly pattern of H500 over the Asian mid-to-high latitudes in JJA 2022.

To further examine the possible contributions of observed SSTAs in 2022 to the H500 anomalies in the summer of 2022 (e.g., Liu et al., 2022), reconstructions of the H500 anomalies during JJA 2022 based on linear regression with the above three detrended SSTA indices are shown in Fig.7d.It can be seen that the reconstructed pattern is similar to the observed one, especially over the Eurasia continent.Specifically, the anomalous positive H500 anomalies over East China and the double-blocking pattern are obtained in the reconstruction, accounting for about a half of the observed anomaly.Therefore, the three tropical oceans exerted cooperative influences on the 2022 extreme HW by inducing the associated atmospheric anomaly in East Asia.

Figure 8 further shows the interannual components of HWs or SAT and the detrended tropical ocean indices.It can be seen that the frequencies of HW days at the interannual time scale have a significant positive correlation with the detrended WPWPS (Fig.8a) and TNA (Fig.8b) indices.The SAT at the interannual time scale has a negative correlation with the detrended IOBM index (Fig.8c).The historical relationships between the tropical oceans and HWs or SAT at XJH are consistent with the case in 2022, implying a possible impact of the three tropical oceans via their influences on atmospheric circulation anomalies.

Fig.8.Interannual components of HW days and the detrended (a) WPWPS and (b) TNA indices,and (c) the interannual components of SAT and the detrended IOBM index during JJA 2000-2021.The numbers in the upper-right corners are the correlation coefficients between the two series in the corresponding panels.

6.Influence of BSISO

The MJO exhibited distinct anomalous propagation and strength during the mid-summer of 2022.As shown in Fig.9a, the MJO was in phase 3 (East Indian Ocean) at the beginning of July 2022.It weakened after one week when it was in phase 5 (Maritime Continent).The persistent and weak MJO did not propagate to the western Pacific (phases 6-7) during the mid-summer, leading to the anomalous frequency of MJO phases 4-5 (Maritime Continent) and 6-7(western Pacific).Quantitatively, the frequencies of phases 4-5 (6-7) during the mid-summer of 2022 were about 1 time more (0.7 times less) than those of their climatology in 1991-2020 (Fig.9b).

Fig.9.(a) Daily variation of MJO phases during JJA 2022; and (b) frequency of MJO phases during JJA 2022 (orange bars) and the 1991-2020 climatology (green bars;%).

BSISO (Lee et al., 2013), especially the canonical northward-propagating variability (BSISO1), usually occurs in conjunction with the eastward-propagating equatorial MJO.The active phases of BSISO1 were influenced by the anomalous propagation of the weak MJO in JJA 2022.As shown in Fig.10a, BSISO1 was concentrated in phases 2-3 (Indian Ocean and East Asia) and 4-5 (Indian and Maritime Continent) during JJA 2022.The BSISO weakened in phase 5 and slowly propagated to the Bay of Bengal and the South China Sea (phases 6-7).As a result, the frequencies of phases 2-5 (Indian Ocean to Maritime Continent) of BSISO1 in 2022 (orange bars in Fig.10b) were higher than the corresponding climatologies in 1991-2020 (green bars in Fig.10b).

The BSISO affects the subtropical circulation (e.g., Liu et al., 2023).The composite intraseasonal OLR and circulation anomalies in BSISO1 phases 2-3 (Fig.11a) and 4-5(Fig.11b) during JJA 1991-2020 show enhanced convection and negative H500 anomalies in the tropical western Pacific and suppressed convection (positive OLR anomalies) and positive H500 anomalies over the YRV, which is a Pacific-Japan-like pattern (Nitta, 1989; Nitta and Hu, 1996).That brought remarkable anomalous subsidence airflow over the YRV, including Shanghai, leading to the warming of SAT and contributing to the extreme HW.Further analysis shows that both the anomalous H500 over the middle and lower reaches of the YRV and the HW days in Shanghai had a significant positive correlation with the frequencies of BSISO1 phases 2-5 (figure omitted).Therefore, the anomalous activity of BSISO1 also contributed to the extreme HW by enhancing the WPSH and its associated subsidence over the YRV, including Shanghai.

Fig.10.As in Fig.9 but for BSISO1.

Fig.11.Composite subseasonal OLR (shading; units: W m-2) and H500 (contour;solid/dashed contours denoting positive/negative values) anomalies in phases (a) 2-3 and (b) 4-5 of BSISO1 during JJA 1991-2020.

7.Summary and discussion

The strongest HW event in China since 1961, with a long duration and extreme strength, hit the YRV particularly hard during the summer of 2022, causing severe drought and a serious shortage in energy supply.By taking the city of Shanghai as an example, the impacts of multiscale variabilities on this extreme HW were examined in this study.

Fig.12.Schematic of the joint influence of the multiscale variabilities on the 2022 extreme HW event in the lower reaches of the YRV.The variabilities include the long-term warming, the multidecadal SSTA associated with the positive-phase of AMO and negative-phase of PDO over mid-to-high latitudes, the interannual SSTA over the three tropical oceans, and the MJO and BISISO together with the atmospheric teleconnection (dashed lines).A and C denote the anticyclone and cyclone of the teleconnection wave train in the mid-to-high latitudes of the Northern Hemisphere, respectively.SSTA+ and SSTA- denote warm and cold SSTAs, respectively.The black eastward and northward arrows denote the influences of the eastward propagation of the MJO and the northward propagation of the BISISO, respectively.

The long-term warming increased the risk of occurrence of the 2022 extreme HW.Quantitatively, the long-term global warming trend may explain about 1/3 of the total HW days with Tmax,2m≥ 35°C during the 2022 extreme HW event in Shanghai in the lower reaches of the YRV.Moreover, the extreme HW was affected by the atmospheric circulation anomaly in association with SSTA at multiple time scales (Fig.12).During mid-summer 2022, the strong WPSH and double-blocking-type circulation anomalies over the Ural Mountains and Sea of Okhotsk were linked to the persistent high-pressure anomaly over the YRV.The high pressure led to the extreme HW via compression of sinking air and enhancement of solar radiation.The positive-phase AMO and negative-phase PDO also contributed to the double-blocking pattern over Eurasian mid-to-high latitudes.

Anomalous warming of both the warm pool of the tropical western Pacific and tropical North Atlantic at the interannual time sale favored the enhancement of the WPSH and the persistence of the double-blocking over Eurasian mid-tohigh latitudes.At the subseasonal time scale, the anomalously frequent phases 2-5 of BSISO1 in association with the frequent phases 4-5 of the weak MJO further strengthened the WPSH by suppressing the low-level convection over the YRV during mid-summer 2022.

The rapid growth of the population and economy in Shanghai has created one of the largest cities in China.According to a recent study by Liang et al.(2022), urbanization may contribute to about one-fifth of the long-term warming in Shanghai.Thus, the probability increase associated with long-term warming in the 2022 extreme HW may also be partially attributable to urbanization.Meanwhile, urban thermal effects can be enhanced in both the canopy and boundary layer under extreme HWs (Ma et al., 2022).Comparison of the spatial distributions of HW frequency during summer 2022 and the 1991-2020 climatology (not shown)suggests that urban thermal effects were strengthened over the west of central city areas in summer 2022.In other words, urban thermal effects may also have strengthened the HW and enlarged its area.How urbanization effects interacted with the 2022 extreme HW deserves further investigation.

Acknowledgements.We appreciate the constructive comments and insightful suggestions from the two reviewers.This work was jointly supported by the Guangdong Major Project of Basic and Applied Basic Research (Grant No.2020B0301030004),the National Natural Science Foundation of China (Grant No.42175056), the Natural Science Foundation of Shanghai (Grant No.21ZR1457600), Review and Summary Project of China Meteorological Administration (Grant No.FPZJ2023-044), the China Meteorological Administration Innovation and Development Project(Grant No.CXFZ2022J009), and the Key Innovation Team of Climate Prediction of the China Meteorological Administration(Grant No.CMA2023ZD03).