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In August 2022, an unprecedented compound heatwave and drought event (CHDE) lasting 24 days occurred in the Yangtze River valley (YRV), leading to a severe reduction of the crop, fresh water, and power supply. We constructed a joint cumulative probability distribution of heatwave and drought intensity, and found that the lowest probability-based index (PI) of 0.06 in 2022 was estimated as a 1-in-662-year event over the 1961–2022 climate. We then detected the fingerprint of greenhouse gas forcing to the observed PI in a generalized extreme value framework, but not the aerosol forcing, suggesting the leading contribution of greenhouse gas forcing on such extreme CHDE. Furthermore, anthropogenic influence had increased the probability of such CHDE by more than 10 times compared to the counterfactual climate. Also, the PI decreased from about 0.30 at the present climate to about 0.14 at the 3 °C global warming level, indicating that CHDE will become more extreme over YRV. [ABSTRACT FROM AUTHOR]

In July-August 2022, Yangtze River Valley (YRV) experienced unprecedented hot summer, with the number of heatwave days exceeding climatology by four standard deviations. The heatwaves and associated severe droughts affected about 38 million people and caused devastating economic losses of about five billion US dollars. Here we present convergent empirical and modelling evidence to show that the record-breaking Pakistan rainfall, along with the 2022 tripe-dip La Niña, produces anomalous high pressure over YRV, causing intense heatwaves. The La Niña-induced second-highest sea surface temperature gradient in the equatorial western Pacific suppresses western Pacific convection and extends the subtropical high westward. More importantly, the tremendous diabatic heating associated with the unprecedented Pakistan rainfall reinforces the downstream Rossby wave train, extending the upper-level South Asia High eastward and controlling the entire YRV. The overlay of the two high-pressure systems sustains sinking motion and increases solar radiation reaching the ground, causing recurrent heat waves. [ABSTRACT FROM AUTHOR]

Using hindcast and forecast data from advanced prediction systems of NCEP CFSv2 and JMA/MRI CPSv2 for the winter 1982/1983–2017/2018, this study investigates the predictability of the climatology and dominant modes of winter-mean surface air temperature (SAT) over East Asia. Although the simulated climatological mean SAT has a large bias over most of the mainland China, both models show high forecasting skills for the principal components (PC1 and PC2) of the two major modes (northern and southern temperature modes) of the winter-mean SAT over East Asia 1 month in advance. In comparison, the MRI-CPSv2 performs better than the CFSv2 in simulating the spatial distribution of the northern temperature mode and the associated circulation anomalies aloft, but the CFSv2 has better performance than the MRI-CPSv2 in simulating the spatial distribution of southern temperature mode and its linkage to the Arctic Oscillation and Eurasian pattern. Furthermore, both models well simulate the impacts of the dipole sea surface temperature (SST) anomalies over the mid-latitude North Pacific and El Niño–Southern Oscillation on the northern and southern temperature modes, respectively. Accordingly, the forecasting skill and signal-to-noise ratio of the PC1 (PC2) are significantly improved in the years with strong SST anomalies over the mid-latitude North Pacific (tropical central-eastern Pacific), particularly for the CFSv2 (MRI-CPSv2) predictions. These results are beneficial for understanding the interannual predictability of the East Asian winter climate. [ABSTRACT FROM AUTHOR]

Using the prediction data from the Global Seasonal Forecast version 5 (GloSea5) during the 1993–2016 period, the probabilistic prediction skills of the midsummer (July and August) surface air temperature (SAT) over the middle and lower reach of the Yangtze River valley (MLYR) are evaluated by the ranked probabilistic skill score. We found that the GloSea5 better predicts below-normal (BN) events than above-normal (AN) events at a long lead time, as well as the associated western Pacific subtropical high (WPSH) and circumglobal teleconnection (CGT) anomaly. This result reveals the asymmetry of the probabilistic prediction skills of the midsummer MLYR SAT, which is related to the asymmetric effect of predictability sources: the warm midsummer ENSO favors BN events via promoting the eastward retreat of the WPSH, whereas the cold midsummer ENSO exhibits weak effect on AN events due to the induced southwestward expansion of the WPSH; the warm mid-latitude North Atlantic (MNA) promotes AN events via projecting its influence onto the atmospheric teleconnections such as the CGT, but not vice versa. The GloSea5 exhibits strong response to both two predictability sources, but fails to reproduce the asymmetric effect of predictability sources especially for ENSO, which limits the prediction skills of AN events. On the other hand, the worse (better) simulations of the warm MNA (warm midsummer ENSO) lead to the lower (higher) prediction skills of AN (BN) events. These results are useful for better understanding the predictability of the midsummer SAT over the MLYR. [ABSTRACT FROM AUTHOR]

The driving mechanism of Arctic amplification (AA) is so complex that no consistent and definitive conclusion has been formed yet. In particular, the internally and externally driven trends of AA have not been distinguished using observation-based methods. Given that the Arctic is more sensitive than other regions to anthropogenic greenhouse gas increases and other external forcings, we focus on separating anthropogenically driven trends from the Arctic surface air temperature (SAT) changes during 1979–2017 to quantify the contribution of anthropogenic effects on AA, with detection and attribution converted to probability distribution functions. Results indicate that the Arctic coast of the Siberian Great Plains, from the Barents Sea to the Kara Sea and eastward to the Bering Strait, has been warming most significantly, and is mainly dominated by anthropogenically driven trends. From 1979 to 2017, the minimum anthropogenically driven warming in most parts of the Arctic Ocean exceeds 2℃, especially the Kara Sea area, where the anthropogenically driven warming is significant, reaching 4℃. In addition, the minimum anthropogenic contributions exceed 60% in most parts of the Arctic Circle and are more than 80% of the warming trend in (75–90° N, 150–180° W). In 140° W–140° E Arctic region, the anthropogenically driven trend is the most remarkable, at 0.82℃ / decade, accounting for 84.5% of the measured warming trend. Meanwhile, the anthropogenically driven trend accelerates most rapidly in this area (0–140° W, 60–90° N). [ABSTRACT FROM AUTHOR]

Persistent extreme heat events (PEHEs) exert a more negative impact on society, including agriculture, plant phenology, power production and human health, compared to general EHEs. The temporal and spatial characteristics of summer PEHEs in eastern China were analysed based on a daily maximum temperature dataset from 759 stations over the period of 1961–2018. The results show the following: Persistent distributions of PEHEs show that they are characterized by an exponential decay with a drop in the decay rate. In terms of spatial distribution, there is an apparent regional difference in the duration of PEHEs. North China is dominated by multi-frequency and short-duration EHEs, while South China is the opposite. PEHEs in North China and the Huanghuai region mainly occur in June-July but mostly in July and August in South China. Strongly responding to global warming, the frequency and duration of PEHEs in North China have increased since the 1990s. However, the frequency of PEHEs in North China and the Huanghuai region has shown opposite trends in June-July since the beginning of the twenty-first century. Affected by the atmospheric circulations, the regional differences in PEHE frequency are also apparent. Since the beginning of the twenty-first century, the PEHEs in North China and the Huanghuai area have shown an increasing trend in August. The short-term PEHEs in the middle and lower reaches of the Yangtze River and South China increased rapidly in the 2000s, while long-term PEHEs increased in the 2010s. This study implies that attention should be paid to not only the frequency of EH days but also to the persistence of EHE which is a key characteristic of damaging EH. [ABSTRACT FROM AUTHOR]

From 21 to 22 July 2012, Beijing and its surrounding areas suffered from an extreme precipitation event that was unprecedented relative to the past 61 years, and the event caused 79 deaths and reported direct economic losses of 11.64 billion Yuan. However, current models have difficulty to simulate the spatial and temporal distribution characteristics of such events. Therefore, improved simulations of these extreme precipitation processes are needed. In this study, nudging methods, including grid nudging (GN) and spectral nudging (SN), and more accurate surface type data retrieved from remote sensing were used in the Weather Research and Forecasting (WRF) model to simulate this extreme precipitation case. When the default city underlay surface of the WRF model was replaced by a more accurate urban surface (NU), the precipitation intensity could be better simulated, but the peak moment of precipitation seriously lagged. Although the peak precipitation intensity simulated by the GN experiment was weak, the simulated precipitation time was basically consistent with the observations. Using GN in only the outside domain could better simulate precipitation peaks, while using GN in both the inside and outside domains could better simulate the spatial distribution characteristics of precipitation. Additionally, the precipitation from GN could be better simulated than that from SN. Overall, the two nudging methods could contribute to better simulations of this case because the nudging methods could improve the simulations of 500-hPa geopotential height, 850-hPa water vapor transport, and low-level weather systems, which are the key factors in adjusting the spatial and temporal distributions of precipitation. This study is the basis for the investigation of the mechanism and attribution of extreme precipitation processes, and the results are of great significance for promoting understanding of and mitigating disasters caused by extreme precipitation. [ABSTRACT FROM AUTHOR]