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Have the onset dates of regional extreme hot events threatening human health been advancing? Based on the daily maximum temperature data from Chinese metrological stations and reanalysis datasets, we quantified the variations in the onset dates of the first regional extreme hot events (EHE-ODs) and revealed the early-onset characteristics of the regional EHEs in southeastern China. The results show that the EHE-ODs in southeastern China have a significant early-onset trend with an average advanced rate of 7.1 days/decade since 1987. The rate in the middle and lower reaches of the Yangtze River and South China exceeds 10.3 days/decade. The interannual variation in the sea ice over the Barents Sea (SICBS) in March and the snow depth over western Eurasia (SDWEA) in April are closely related to the first regional EHE-ODs in southeastern China, which both provide favorable atmospheric circulation patterns and significant descending motion for the occurrence of regional EHEs. The pathway of the physical process originates from the northern Eurasia propagating southeastward, and spreads to southeastern China, which can enhance the anticyclonic anomaly in southeastern China and favor the occurrence of regional EHEs in southeastern China in the early summer. Therefore, the SICBS in March might be a critical potential predictor for the onset of the regional EHEs in southeastern China. • The onset date of extreme hot events (EHE) in southeastern China has an early-onset trend. • The advanced rate of the early-onset trend of regional EHE exceeds 10.3 days/decade. • Barents Sea ice plays as a precursor for the onset date of EHE in eastern China. • Midlatitude Eurasian atmospheric wave train is related to the early onset of EHE. [ABSTRACT FROM AUTHOR]

Accurate short-term climate predictions, specifically the prediction of dominant rain belts during flood seasons, represent a significant challenge, particularly in the absence of notable external forcing signal anomalies in the previous winter. To address this challenge, we propose a novel climate index based on an Auto-Encoder (AE) Neural Network that exhibits superior performance in indicating several significant precipitation events in the Yangtze and Huaihe River Basins under varying El Niño conditions. Notably, the novel climate index rectifies the discrepancy between the 2019/2020 Niño index and the 2020 summer precipitation. In order to understand the information source of AE, the causality between AE and other indices was further analyzed by Granger Causality. The results show that AE index mainly contains the information of subtropical high index and ocean index, which is consistent with previous studies. • AE obtained by Auto-encoder can make up for the "failure" of Nino index in abnormally heavy precipitation events. • In terms of magnitude and state, AE has better description of precipitation than Nino3.4. • The main indices of AE information are subtropical high-pressure index and sea surface temperature index. [ABSTRACT FROM AUTHOR]

By using the one-month‑lead predictions from the Climate Forecast System version 2 (CFSv2), this study evaluates the predictability of the unprecedented heatwaves hitting the Yangtze River Valley (YRV) and Tibetan Plateau (TP) in 2022 late summer. CFSv2 successfully reproduces the central China heatwaves, but shows distinct performances between the YRV and TP. Specifically, CFSv2 exhibits stronger ability to capture TP heatwaves that is close to the highest historical prediction record, whereas the predicted extremity of the YRV heatwaves is underestimated compared to the observations. The accurate simulation of the intense upper-level circumglobal teleconnection (CGT) pattern, particularly for the East Asia CGT center (CGT EA), is essential for the success predictions of high temperature anomalies, due to the strong and accurate linkage between CGT EA and YRV/TP heatwaves in CFSv2. The intense CGT pattern is associated with a stronger model response to developing La Niña in CFSv2, quite different from the observed leading contribution from the unprecedented Pakistan rainfall. The impacts of developing La Niña and Pakistan rainfall on the intense CGT pattern are significantly overestimated and underestimated by CFSv2, respectively. Additionally, the La Niña-forced enhancement of lower-level western Pacific subtropical high (WPSH) plays another important role in YRV heatwaves, but the reproduction of WPSH intensity is unable to reflect YRV heatwaves in CFSv2 due to the southeastward shift of the predicted WPSH location, which should be responsible for the underestimated YRV heatwaves. The model improvements in aspect of the Pakistan rainfall, El Niño-Southern Oscillation–CGT linkage and lower-level WPSH location should help effectively predict the central China heatwaves in the future. • CFSv2 successfully predicts the central China heatwaves in 2022 late summer. • The accurately predicted CGT is essential for the reproduction of heatwaves. • La Niña leads to the intense CGT in CFSv2 rather than Pakistan rainfall in observation. [ABSTRACT FROM AUTHOR]

In this paper, we investigated the spatio-temporal characteristics of the amount of light rain and the number of days with light rain in the ESWC and explored the possible causes of the significant decrease in the number of days with light rain. The results show that: (1) Both the amount of light rain and the number of days with light rain over the ESWC in summer showed a decreasing trend, with linear trends of −1.5 mm/decade and −0.88 day/decade, respectively. The decrease in the number of days with light rain was more distinct than the decrease in the amount of light rain. (2) The decrease in the number of days with light rain was closely related to the decrease in the relative humidity caused by an increase in temperature and specific humidity. The trend in relative humidity resulting from an increase in temperature alone was −1.1%/decade, whereas the trend in relative humidity caused by an increase in specific humidity alone was 0.29%/decade. The trend in relative humidity was −0.76%/decade. The decrease in relative humidity was mainly a result of an increase in temperature. (3) The increase in temperature resulted in a decrease in the relative humidity and an increase in the surface evaporation. The increase in evaporation increased the specific humidity, which, in turn, increased the relative humidity. This compensated for the decrease in the relative humidity caused by the increase in the temperature and further decelerated the decrease in the number of days with light rain. • The number of summer days with light rain in the ESWC decreased significantly during 1961–2018. • The decrease in the number of days with light rain was closely related to the decrease in the relative humidity. • The trend of relative humidity made by an increase in temperature alone was −1.1%/decade. • The increase in evaporation compensated for the decrease in relative humidity caused by the increase in temperature. [ABSTRACT FROM AUTHOR]