Data and methods

The observation data used in this study consist of monthly surface air temperature and precipitation with a resolution of 0.5° × 0.5° from 1961 to 2022. The data were retrieved from over 2,000 quality-controlled weather stations in China (Wu and Gao 2013), covering the Yangtze River basin (about 105°–123°E and 28°–35°N, black box in Fig. 1a). We use the geopotential height anomalies at 500 hPa relative to 1991–2020 climatology from the NCEP–NCAR reanalysis to describe the circulation background of this event.

We also use data from six CMIP6 models run under both natural and anthropogenic forcings (ALL) and natural-only forcing (NAT), respectively, during 1961–2020 (Eyring et al. 2016). As the 2015–20 segments of NAT simulations used natural forcing from the shared socioeconomic pathway 2-4.5 (SSP2-4.5) scenario, the historical ALL simulations, normally ended in 2014, were simply completed to 2020 with their corresponding SSP2-4.5 scenario runs. Our practice here is identical to Hu et al. (2023). We interpolated monthly precipitation and surface air temperature from different models onto the grid of observation dataset using bilinear interpolation. The basic information of models used is listed in Table S1 in the online supplemental material (https://doi.org/10.1175/BAMS-D-23-0149.2).

In this work, we are also interested in the anthropogenic influence on the likelihood of a hot event occurring while a configuration of dryness (X₁ = x₁) is fixed. This can be derived from the conditional probability density distribution: $f(x_2|x_1)=c[{\text{F}}_1(x_1),F_2(x_2)]\times f_2(x_2)$, where c is the copula probability density function (PDF) and f₂(x₂) is the PDF of the STI. Once the conditional marginal PDF is constructed, the probability of STI exceeding a specific threshold can be calculated (Chiang et al. 2021).

The fraction of attributable risk (FAR = 1 – P_NAT/P_ALL) is used to estimate the effect of anthropogenic influence on the occurrence probability of the 2022 event (Fischer and Knutti 2015; Stott et al. 2016). The terms PNAT and PALL represent the probability of the event under natural forcing and historical forcing, respectively. FAR can be interpreted as the fraction of the likelihood of an event that is attributable to the anthropogenic forcing. A bootstrap resampling was performed 1,000 times to estimate the 90% confidence interval (CI) of FAR as in Christidis et al. (2013).

Results

The majority of the study domain experienced extreme hot and dry conditions in 2022 (Figs. 1a,b). The atmospheric circulation provided favorable background for these conditions. The western Pacific subtropical high (WPSH) was anomalously westward shifted in June, July, and August (Fig. S1). The entire basin was controlled by a strong positive anomalies of geopotential height at 500 hPa (Fig. 1c), corresponding to the peak of dry conditions in August. Such an anomalous circulation pattern prevented cold air in the north from moving southward. Simultaneously, in the region controlled by anomalous high pressure, prevailing descending air inhibits precipitation and enhances warming through subsidence.

The average STI and SPI over the domain show significant correlation (r = –0.28, p < 0.05), resulting from the influences of the land–atmosphere interaction and the circulation anomalies. The main variability of SPI is at the interannual scale. For STI, however, there is a clear decadal variability, characterized by decreasing trends before the 1990s and increasing trends afterward. The domain-averaged STI of 2022 reaches 1.88, breaking the observation record since 1961 (Figs. 1d,e). The PDF deduced from the historical observations indicates that the probability of such a hot event is nearly 0.007 (Fig. 1d), corresponding to a return period of 150 years, with a 90% confidence interval of 59–721 years. The domain-averaged SPI of 2022 is –0.6 (Figs. 1d,e), indicating a severe meteorological drought with a probability of 0.1. Such a dry event is a 1-in-10-yr event (90% confidence interval: 1 in 7–17 years). In terms of a compound event combining both heat and dryness, a decrease of SHDI indicating an increase in the severity of a compound event was observed since the 1990s, and the severity of the 2022 compound event also largely broke the historical record (Fig. 1e). The best copula fit suggests that the return period of the 2022 hot and dry event is 753 years (533–1,151 years for the 90% confidence interval; Fig. 1f). It is to be noted that the longer return period of the compound event, relative to the single event, may be related to the heat–dryness negative correlation (Hao et al. 2022).

The performance of models in simulating STI and SPI over the Yangtze River basin is first assessed before conducting the attribution analysis. The CMIP6 models can well simulate the increases in the severity of compound events since the 1990s with a weak interannual variability (Fig. S2a). The CMIP6 models can also simulate the probability distribution of observed SPI and STI quite well, as shown in Figs. S2b and S2c. The distributions of both STI and SPI in the model and observation cannot be distinguished with the Kolmogorov–Smirnov (K-S) test (p value > 0.05). Then, we compare the probability of STI and SPI events similar to the one in 2022 in ALL and NAT simulations to quantify the impact of anthropogenic factor. For STI, the ALL forcing causes the PDF to generally shift to the right (Fig. 2a). Under NAT forcing, the probability of a heatwave event similar to the 2022 event is very low, estimated at 0.00035. However, under ALL forcing, this probability increases to 0.0087. In other words, human influence has increased the probability of the record-breaking heatwave in 2022 by nearly 25 times and explained 96% (90% CI: 90%, 99%) of the attributable risk for the event (Fig. 2d).

Attribution and projection results. The probability density function (PDF) of domain-averaged (a) STI, (b) SPI, and (c) bivariate return period of compound event for ALL forcing (red line) and NAT forcing (blue line); the selected copula function is rotated Clayton 270° for NAT and rotated and Tawn type 1 90° for ALL, respectively. (d) The best estimation (blue line) and 90% confidence interval (gray shadings) of the fraction of attributable risk (FAR) for SPI, STI, and the compound event. (e) The PDF of domain-averaged SPI, and (f) the bivariate return period of the compound event for historical forcing (blue line) and future SSP2-4.5 forcing (red line); the selected copula function is rotated Clayton 270° for future forcing. The black dashed lines and triangles denote the 2022 event.

Citation: Bulletin of the American Meteorological Society 104, 11; 10.1175/BAMS-D-23-0149.1

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Attribution and projection results. The probability density function (PDF) of domain-averaged (a) STI, (b) SPI, and (c) bivariate return period of compound event for ALL forcing (red line) and NAT forcing (blue line); the selected copula function is rotated Clayton 270° for NAT and rotated and Tawn type 1 90° for ALL, respectively. (d) The best estimation (blue line) and 90% confidence interval (gray shadings) of the fraction of attributable risk (FAR) for SPI, STI, and the compound event. (e) The PDF of domain-averaged SPI, and (f) the bivariate return period of the compound event for historical forcing (blue line) and future SSP2-4.5 forcing (red line); the selected copula function is rotated Clayton 270° for future forcing. The black dashed lines and triangles denote the 2022 event.

Citation: Bulletin of the American Meteorological Society 104, 11; 10.1175/BAMS-D-23-0149.1

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Attribution and projection results. The probability density function (PDF) of domain-averaged (a) STI, (b) SPI, and (c) bivariate return period of compound event for ALL forcing (red line) and NAT forcing (blue line); the selected copula function is rotated Clayton 270° for NAT and rotated and Tawn type 1 90° for ALL, respectively. (d) The best estimation (blue line) and 90% confidence interval (gray shadings) of the fraction of attributable risk (FAR) for SPI, STI, and the compound event. (e) The PDF of domain-averaged SPI, and (f) the bivariate return period of the compound event for historical forcing (blue line) and future SSP2-4.5 forcing (red line); the selected copula function is rotated Clayton 270° for future forcing. The black dashed lines and triangles denote the 2022 event.

Citation: Bulletin of the American Meteorological Society 104, 11; 10.1175/BAMS-D-23-0149.1

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• Download figure as PowerPoint slide

For SPI, anthropogenic forcing causes the entire PDF to shift to the left, indicating that anthropogenic forcing enhances dryness in the Yangtze River basin (Fig. 2b). Under NAT forcing conditions, the probability of a dry event similar to the 2022 event is 0.073. Under ALL forcing, this probability increases to 0.107. The risk of a dry event in the Yangtze River basin reaching or exceeding the intensity of 2022 under historical forcing is nearly 1.5 times larger compared to natural forcing, of which 32% (90% CI: 21%, 42%) can be attributed to human influence (Fig. 2d).

For the compound event (Fig. 2c), the 2022-like compound event has a high recurrence interval of nearly 3,500 years under natural world forcing, whereas it is a much more frequent 1 in 500 years in the actual world. Human activities have increased the likelihood of this record-breaking compound event by 7 times, of which 85.5% (90% CI: 66%, 96%) can be attributed to human activities (Fig. 2d). When compared to FAR for STI, it is evident that the occurrence of this compound event is predominantly driven by heat induced by anthropogenic influence.

It is worthy of note that such conclusions could not be directly extrapolated to a future scenario. Actually, we also performed the same analysis (results shown in Figs. 2e,f) with historical (1961–2020) and SSP2-4.5 (2041–2100) simulations of the same models as we used to deal with the pair NAT/ALL. As expected for STI, the probability for heat increases from 0.0087 in “historical” to 0.97 in “SSP2-4.5.” It is a little surprising to see that for SPI the probability of dryness decreases from 0.107 to 0.038, showing rainfall increases in the last half of the century (Fig. 2e). This is consistent with most other studies investigating future rainfall changes in the Yangtze River basin (Wang et al. 2014), a consequence of an enhanced East Asian summer monsoon (EASM) circulation. We suspect that the drying in ALL (compared to NAT) was mainly provoked by the important aerosol forcing, as demonstrated in Zhang et al. (2020) and Qian et al. (2009). The aerosol effects that contribute to the weakening of the EASM under ALL forcing have also been accounted for in CMIP6 models (Monerie et al. 2022). The likelihood of a compound hot and dry event, similar to the one in 2022, is expected to increase in the Yangtze River basin (Fig. 2f); the return periods from the historical and future simulations show a considerable shift from 1 in 500 years to 1 in 27 years. Nevertheless, these events are projected to be less severe in terms of dryness compared to the historical period, indicating that the compound event is primarily driven by heat, rather than dryness.

Dry conditions are generally favorable for hot events to occur, since the surface energy partition is altered. It is thus interesting to investigate the occurrence probability of a hot event like that of 2022 under a given dryness, and the fractional contribution of human activities to this conditional event. We found that human activities indeed increased the likelihood of a hot event (STI being greater than 1.88) given dry conditions (SPI being –0.6). The probability of this conditional event is 0.0052 for natural forcing. It increases to 0.017 for historical forcing (Fig. S2a). This gives a FAR of 70% (90% CI: 41%, 95.7%), implying that the anthropogenic influence explains 70% of the attributable risk for this conditional event. Additionally, we found that the best estimated FAR of the conditional event increases as STI increases (Fig. S2b), which means that human influence may lead to the occurrence of more intense hot events.

Conclusions

In the summer of 2022, an exceptional hot and dry compound event occurred in the Yangtze River basin in China. We found that anthropogenic effects have increased the occurrence probability of both heat and dryness as independent events, and compound events involving them together. Specifically, a fraction of 32% (FAR) for the individual dryness and a fraction of near 96% for the individual heat is attributable to anthropogenic forcing. For the compound event, the fraction of attributable risk in relation to human activities is 86%. Based on the attribution, the compound event is primarily driven by heat, rather than dryness. This is even more evident for the future, as the probability of dryness occurrence will decrease in this region, while the probability of a compound event will continue to increase.