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Accurate subseasonal prediction of heavy rainfall is helpful for disaster mitigation but challenging. The land thermal condition of Tibetan Plateau (TP), usually with climate memory ranging from weeks to seasons, has been seen as a potential predictability source for subseasonal prediction. Aiming at 2020 record‐breaking Mei‐yu rainfall, this study attempts to investigate whether and how the influence of initial TP surface thermal condition near late June influences the July rainfall prediction over the Middle and Lower Yangtze River Region (MLYR), based on two contrasting prediction experiments using a global climate ensemble prediction system. The results show that the most distinguishable change in the downstream prediction in July is the anomalous low‐tropospheric cyclone and the associated increased rainfall over MLYR corresponding to the warmer initial condition of surface TP. Influenced by the invasion of the positive potential vorticity (PV) center that generated over TP and propagated eastward, this low‐level cyclone anomaly over MLYR is formed within the first week of prediction, and persists for the next 3 weeks maintained by the positive feedback between the low‐level cyclone and middle‐tropospheric latent heating over MLYR in the prediction. This study confirmed the significant effect of TP initial thermal condition on downstream prediction ahead of 3 weeks during the Mei‐yu season (peak summer) with strong land–atmosphere coupling over TP. Plain Language Summary: Based on two contrasting prediction experiments using a global climate ensemble prediction system, this study exhibits that changing the initial thermal condition over Tibetan Plateau (TP) surface can significantly influence the subseasonal prediction of rainfall over Middle and Lower Yangtze River Region (MLYR) in Mei‐yu season. After the TP land surface warming at the initial time of prediction, a low‐level cyclonic and increased rainfall appears within the first week of prediction and persists for the next 3 weeks maintained by the positive feedback between the low‐level cyclone and middle‐tropospheric latent heating over MLYR. This study confirmed the effect of initial land thermal condition over TP on the MLYR prediction ahead of 3 weeks during Mei‐yu season (peak summer) with strong land‐atmosphere coupling over TP. Key Points: Initial TP land surface warming can generate a local near‐surface PV positive center and expand downstream within the first prediction weekThe downstream cyclonic anomaly and increased rainfall can be maintained for 3 weeks in prediction by a positive feedbackThe study confirmed the effect of TP initial thermal condition on the MLYR prediction ahead of 3 weeks in Mei‐yu season (peak summer) [ABSTRACT FROM AUTHOR]

Background: Short‐term exposure to ambient air pollution has been linked to increased risk of stroke mortality, but its adverse effects on mortality from specific types of stroke including ischemic stroke and hemorrhagic stroke remain poorly understood. Methods: Using the China National Mortality Surveillance System, we conducted a time‐stratified case‐crossover study among 412,567 stroke deaths in Jiangsu province, China during 2015–2019. Residential daily PM2.5, PM10, SO2, NO2, CO, and O3 exposure concentrations were extracted from the ChinaHighAirPollutants dataset for each subject. Conditional logistic regression models were performed to conduct exposure–response analyses. Results: Each 10 μg/m3 increase of PM2.5, PM10, SO2, NO2, CO, and O3 was respectively associated with a 1.44%, 0.93%, 5.55%, 2.90%, 0.148%, and 0.54% increase in odds of mortality from ischemic stroke, which was significantly stronger than that from hemorrhagic stroke (percent change in odds: 0.74%, 0.51%, 3.11%, 1.15%, 0.090%, and 0.10%). The excess fraction of ischemic stroke mortality associated with PM2.5, PM10, SO2, NO2, CO, and O3 exposure was 6.90%, 6.48%, 8.21%, 8.61%, 9.67%, and 4.76%, respectively, which was also significantly higher than that of hemorrhagic stroke mortality (excess fraction: 3.49%, 3.48%, 4.69%, 3.48%, 5.86%, and 0.88%). These differences in adverse effects generally remained across sex, age, and season. Conclusions: Short‐term exposure to ambient air pollution was significantly associated with increased risk of both ischemic and hemorrhagic stroke mortality and posed considerable excess mortality. Our results suggest that air pollution exposure may lead to substantially greater adverse effects on mortality from ischemic stroke than that from hemorrhagic stroke. [ABSTRACT FROM AUTHOR]

During 1998–2013, global surface air temperature increased more slowly than greenhouse gas concentrations, and therefore this period was defined as a "global warming hiatus." The characteristics of climate extremes in China during this period are still not clear. In this study, based on 2,841 meteorological station observations in China, the trends of precipitation and temperature extremes during the global warming hiatus were investigated relative to the reference period (RF) (1982–1997) and the whole historical period (HIS) (1982–2017). Machine learning was used to capture the nonlinear regression of climate extremes, and the Theil–Sen method with the Mann–Kendall significant test was used to calculate the slopes of their linear trends. During the global warming hiatus, annual and summer (JJA) warmest days TXx and the number of summer days SU over the whole of China and most of its four subregions were found to decrease relative to the HIS and RF periods. Annual and winter (DJF) coldest nights TNn over China and its four subregions were found to decrease moderately relative to the HIS and RF periods, whereas the number of frost days FD increased consistently. Precipitation extremes showed more temporal and spatial variability than temperature extremes. Trends of annual and JJA wet extremes during the hiatus were found to decrease relative to the HIS and RF periods, whereas the dry extreme index during the hiatus was found to increase generally over China and in most subregions. Lighter winds and lower relative humidity over most areas of China might have contributed to less pronounced trends of wet extremes during the hiatus period. [ABSTRACT FROM AUTHOR]

The China Meteorological Administration recently released its first reanalysis product, the CRA40 dataset covering the time period 1979–2018. We evaluate the performance of the CRA40 dataset in reproducing the observed precipitation climatology and variations over the global domain and typical monsoon regions during the period 1980–2018. In this study, four additional reanalysis precipitation datasets—the European Centre for Medium‐Range Weather Forecasts Reanalysis v5 (ERA5), Japanese 55‐year reanalysis (JRA55), Modern‐Era Retrospective Analysis for Research and Applications version 2 (MERRA2) and Climate Forecast System Reanalysis—are compared against the Global Precipitation Climatology Project (GPCP). For the global climatology, there are considerable differences in the performance of the five reanalysis products over south‐central Asia (85–95°E, 20–40°N), where CRA40 exhibits the wettest bias across all months especially for the south of 30°N bands. JRA55 produces more intense precipitation with a larger bias over the Atlantic and Pacific intertropical convergence zones (ITCZs). Meanwhile, CRA40 tends to slightly underestimate low rainfall, but overestimates high rainfall. In terms of trend, except MERRA2, the average global/hemispheric precipitation increased by 0.02–0.11 mm/day/decade in all the reanalysis datasets, which is attributed to clearly increased trends along the ITCZ. In contrast, a non‐significant increasing trend is observed. JRA55 generally has the empirical orthogonal function pattern most consistent with the GPCP, with the highest spatial and temporal correlations. Moreover, CRA40 correlates better with the Niño 3.4 index, producing precipitation patterns similar to the El Niño–Southern Oscillation. For six typical monsoon systems, CRA40 has a less consistent local precipitation average with a larger normalized standard deviation and centred root‐mean‐square error, and lower correlation coefficient than other reanalyses when compared with the GPCP. [ABSTRACT FROM AUTHOR]

The Tibetan Plateau has experienced substantial warming during the last few decades. As a result, the cryosphere of the Tibetan Plateau, including the snow cover, has shown significant changes. We characterized the changes in snow over the Tibetan Plateau using several snow‐related indices, including snow depth and snow‐covered days based on the MERRA‐2 and JRA‐55 reanalysis data set and passive microwave (MW) satellite observations. The analyses were performed over different time periods—including the last 38, 30, 20, and 10 snow seasons—from 1980 to 2018. The results show that the significant trends in snow depth and snow‐covered days are dependent on both the season and region and vary with the data set and snow‐related index used. There was a clear decrease in the annual maximum consecutive snow‐covered days (CSCDMax), and this was characterized by a later begin time and an earlier end time for both MERRA‐2 and JRA‐55, but only a later begin time for MW during the time period 1980–2018. The changes in CSCDMax were generally dominated by changes in the begin time, which were mainly caused by the increase in the average 2‐m air temperature for November–December. The relatively weak trends in the end time were a result of the combined impact of warmer temperatures and more precipitation in March–April. The CSCDMax averaged over the Tibetan Plateau showed decreasing trends in all studied time periods for JRA‐55 and MW and in the last 38 and 10 snow seasons for MERRA‐2 during the time period 1980–2018. Key Points: The changes in snow show remarkable regional and seasonal differences and vary with data set and snow‐related index usedMost of the time series during 1980–2018 show a reduction in snow depth and snow‐covered days averaged over the Tibetan PlateauThe trends in the begin time of the annual maximum consecutive snow‐covered days are relatively stronger than in the end time [ABSTRACT FROM AUTHOR]

The spatiotemporal pattern of precipitation is significantly changing with global climate change. Snowfall is a solid phase of precipitation and an important water resource. With two gridded data sets of APHRO (Asia Precipitation‐Highly‐Resolved Observational Data Integration Towards Evaluation of Water Resources) and CN05.1, this study analyses the changes in the spatiotemporal pattern of snowfall in a snow‐dominant region of China from 1961 to 2015. The results indicate the significant increasing trend of winter snowfall in horizontal and altitude dimension in snow‐dominant regions, but the winter snowing season length shortened. For the frequency of snowfall intensity level, light, and heavy snowfall and snowstorms increased, but moderate snowfall showed no change. However, the intensity of extreme snowfall in once‐in‐a‐century was decreasing in all of the snow‐dominant regions. In the altitude dimension, the increasing trend in snow‐dominant conditions was not uniform, which may be related to change in air temperature and water vapour through the vertical atmospheric levels. The upwards trend in snowfall may be caused by the increase of atmospheric water content rather than the change of snowy weather conditions. In addition, the change values of climate indices can also contribute to snowfall increasing in snow‐dominant regions. [ABSTRACT FROM AUTHOR]