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Human-perceived thermal comfort (known as human-perceived temperature) measures the combined effects of multiple meteorological factors (e.g., temperature, humidity, and wind speed) and can be aggravated under the influences of global warming and local human activities. With the most rapid urbanization and the largest population, China is being severely threatened by aggravating human thermal stress. However, the variations of thermal stress in China at a fine scale have not been fully understood. This gap is mainly due to the lack of a high-resolution gridded dataset of human thermal indices. Here, we generated the first high spatial resolution (1 km) dataset of monthly human thermal index collection (HiTIC-Monthly) over China during 2003–2020. In this collection, 12 commonly used thermal indices were generated by the Light Gradient Boosting Machine (LightGBM) learning algorithm from multi-source data, including land surface temperature, topography, land cover, population density, and impervious surface fraction. Their accuracies were comprehensively assessed based on the observations at 2419 weather stations across the mainland of China. The results show that our dataset has desirable accuracies, with the mean R2, root mean square error, and mean absolute error of 0.996, 0.693 ∘C, and 0.512 ∘C, respectively, by averaging the 12 indices. Moreover, the data exhibit high agreements with the observations across spatial and temporal dimensions, demonstrating the broad applicability of our dataset. A comparison with two existing datasets also suggests that our high-resolution dataset can describe a more explicit spatial distribution of the thermal information, showing great potentials in fine-scale (e.g., intra-urban) studies. Further investigation reveals that nearly all thermal indices exhibit increasing trends in most parts of China during 2003–2020. The increase is especially significant in North China, Southwest China, the Tibetan Plateau, and parts of Northwest China, during spring and summer. The HiTIC-Monthly dataset is publicly available from Zenodo at https://doi.org/10.5281/zenodo.6895533 (Zhang et al., 2022a).

Surface ozone (O3) pollution during summer (June–August) over eastern China has become more severe in recent years, resulting in a co-occurrence of surface O3 and PM2.5 (particulate matter with aerodynamic diameters ≤ 2.5 µm in the air) pollution. However, the mechanisms regarding how the synoptic weather pattern (SWP) might influence this compound pollution remain unclear. In this study, we applied the T-mode principal component analysis (T-PCA) method to objectively classify the occurrence of four SWPs over eastern China, based on the geopotential heights at 500 hPa during summer (2015–2018). These four SWPs over eastern China were closely related to the western Pacific subtropical high (WPSH), exhibiting significant intra-seasonal and interannual variations. Based on ground-level air quality observations, remarkable spatial and temporal disparities of surface O3 and PM2.5 pollution were also found under the four SWPs. In particular, there were two SWPs that were sensitive to compound pollution (Type 1 and Type 2). Type 1 was characterized by a stable WPSH ridge with its axis at about 22∘ N and the rain belt located south of the Yangtze River Delta (YRD); Type 2 also exhibited WPSH dominance (ridge axis at ∼ 25∘ N) but with the rain belt (over the YRD) at a higher latitude compared to Type 1. In general, SWPs have played an important role as driving factors of surface O3–PM2.5 compound pollution in a regional context. Our findings demonstrate the important role played by SWPs in driving regional surface O3–PM2.5 compound pollution, in addition to the large quantities of emissions, and may also provide insights into the regional co-occurring high levels of both PM2.5 and O3 via the effects of certain meteorological factors.

Human thermal comfort measures the combined effects of temperature, humidity, and wind speed, etc., and can be aggravated under the influences of global warming and local human activities. With the most rapid urbanization and the largest population, China is being severely threatened by aggravating human thermal stress. However, the variations of thermal stress in China at a fine scale have not been fully understood. This gap is mainly due to the lack of a high-resolution gridded dataset of human thermal indices. Here, we generate the first high spatial resolution (1 km1 km) dataset of monthly human thermal index collection (HiTIC-Monthly) over China from 2003 to 2020. In this collection, 12 commonly used thermal indicators are generated by the LGBM machine learning algorithm from multi-source gridded data, including MODIS land surface temperature, topography, land cover and land use, population density, and impervious surface fraction. Their accuracies were comprehensively assessed based on observations at 2419 weather stations across the mainland of China. The results show that our dataset has desirable performance, with mean R2, root mean square error, mean absolute error, and bias of 0.996, 0.693 °C, 0.512 °C, and 0.003 °C, respectively, by averaging the 12 indicators. Moreover, the predictions exhibit high agreements with observations across spatial and temporal dimensions, demonstrating the broad applicability of our dataset. The comparison with two existing datasets also suggests that our high-resolution dataset can describe a more explicit spatial distribution of the thermal information, showing great potentials in fine-scale (e.g., intra-urban) study. Further investigation reveals that nearly all indicators exhibit increasing trends in most parts of China during the year 2003~2020. The increase is especially stronger in North China, Southwest China, the Tibetan Plateau, and parts of Northwest China, and in the spring and summer seasons. The HiTIC-Monthly dataset is publicly available via https://zenodo.org/record/6895533 (Zhang et al., 2022a).

Heatwaves (HWs) paired with higher ozone (O3) concentration at the surface level pose a serious threat to human health. Their combined modulation of synoptic patterns and urbanization remains unclear. Using 5 years of summertime temperature and O3 concentration observation in Beijing, this study explored potential drivers of compound HWs and O3 pollution events and their public health effects. Three favorable synoptic weather patterns were identified to dominate the compound HWs and O3 pollution events. These weather patterns contributing to enhance those conditions are characterized by sinking air motion, low boundary layer height, and high temperatures. Under the synergy of HWs and O3 pollution, the mortality risk from all non-accidental causes increased by approximately 12.31 % (95 % confidence interval: 4.66 %, 20.81 %). Urbanization caused a higher risk of HWs and O3 in urban areas than at rural stations. Particularly, due to O3 depletion caused by NO titration at traffic and urban stations, the health risks related to O3 pollution in different regions are characterized as follows: suburban stations > urban stations > rural stations > traffic stations. In general, favorable synoptic patterns and urbanization enhanced the health risk of these compound events in Beijing by 33.09 % and 18.95 %, respectively. Our findings provide robust evidence and implications for forecasting compound HWs and O3 pollution events and their health risks in Beijing or in other urban areas all over the world that have high concentrations of O3 and high-density populations.

Heavy air pollution is strongly influenced by weather conditions and is thus sensitive to climate change. Especially, for the areas with complex topography such as the Sichuan Basin (SB), one of the most polluted areas of China, the synergistic effects of synoptic weather patterns and topography on air quality are unclear and warrant investigation. This study examined the typical synoptic patterns of SB in winter days of 2013–2017 and revealed their synergistic effects with topography on air quality. Three categories of synoptic patterns including dry low-trough, high-pressure, and wet low-vortex patterns accompanying heavy, medium, and slight air pollution, respectively, were identified. In particular, the dry low-trough patterns occur most frequently, accounting for around 62% of the total days. In the case of this pattern, westerly wind prevails over the SB and the aloft atmosphere is warmer than the Tibetan Plateau (TP) at the same height, which induces the cold air over TP moving eastward to the SB. Under the synergistic effects of the cold air eastward movement and TP, a strong descending motion (known as foehn) is observed on the leeward slope of the towering TP. This foehn warming causes a stable layer above the planetary boundary layer (PBL), which suppresses secondary circulation and PBL. These features restrict atmospheric pollutant dispersion, resulting in poor air quality. In contrast, for the high-pressure and wet low-vortex patterns, cold air masses from the north invade southward and cover the northwest SB. This invasion remarkably decreases the atmospheric stability of the lower troposphere, deepens the PBL, and enhances the height of secondary circulation, thereby facilitating air pollutant dispersion. Moreover, the wet low-vortex pattern is accompanied by frequent precipitation events (with 80% rainy days), further bringing down air pollution levels. These findings provide an insight for improving air pollution forecast in the complex terrain areas under global warming.

Synoptic weather conditions can modulate short-term variations in the marine biophysical environment. However, the impact of large-scale synoptic circulation patterns (LSCPs) on variations in chlorophyll-a (chl-a) and sea surface temperature (SST) in the South China Sea (SCS) remains unclear. Using a T-mode principal component analysis method, four types of LSCP related to the Northwest Pacific subtropical high are objectively identified over the SCS for the summers of 2015–2018. Type 1 exhibits a lower chl-a concentration of &lt, 0.3 mg m−3 offshore of southern Vietnam with respect to the other three types. For Type 2, the high chl-a concentration zone (&gt, 0.3 mg m−3) along the coast of Guangdong exhibits the widest areas of coverage. The offshore chl-a bloom jet (&gt, 0.3 mg m−3) formed in southern Vietnam is the most obvious under Type 3. Under Type 4, the high chl-a concentration zone along the coast of Guangdong is the narrowest, while the chl-a concentration in the middle of the SCS is the lowest (&lt, 0.1 mg m−3). These type differences are mostly caused by the various monsoon circulations, local ocean mesoscale processes and resultant differences in localized precipitation, wind vectors, photosynthetically active radiation and SST. In particular, precipitation over land helps to transport nutrients from the land to the shore, which is conducive to the increase of chl-a. However, precipitation over ocean will dilute the upper seawater and reduce chl-a. Typhoons pump the deeper seawater with nutrients to the surface, and therefore make a positive contribution to chl-a in most offshore areas, however, they also disturb shallower water and hinder the growth of phytoplankton, making a negative contribution near the coast of Guangdong. In general, our findings will provide a better understanding of wind pump impact: the responses of marine biophysical environments to LSCPs.

As one of the aridest regions in the mid-latitudes, arid Northwest China (ANC) has scarce water resources year-round, its annual total precipitation is mainly contributed by summer extreme precipitation events, which may also cause disastrous flash floods. However, the physical mechanism and climatic features of extreme precipitation events in ANC are not fully understood and warrant in-depth investigations. Taking Xinjiang as an example, this study investigates the underlying mechanisms and long-term trend of extreme precipitation events in ANC during 1961‒2019 using observational and reanalysis datasets. Results suggest that summer extreme precipitation events in ANC are characterized by a zonal wave pattern with the deepening of the western Siberian trough, central Asian high, and Mongolian high. Under the effects of the zonal wave pattern, the westerly and the easterly airflows anomalously intensify and converge in ANC, transporting a large amount of water vapor from the Arctic Ocean, the Caspian Sea, the Aral Sea, the Arabian Sea, and eastern China to ANC. Meanwhile, a deep anomalous cyclone center induced by the deep wave pattern appears over ANC, thus providing favorable conditions for the occurrence of extreme precipitation events in ANC. Over the long-term period, total summer precipitation and the frequency of extreme precipitation events in ANC show significant increasing trends, indicating that summer climate in ANC is becoming more humid, and extreme precipitation events are becoming more frequent. These findings provide scientific insights for understanding the formation mechanism of summer extreme precipitation events and mitigating the corresponding disasters in arid regions.

Low-level jet (LLJ) significantly affects the synoptic-scale hydrometeorological conditions in the South China Sea, although the impact of LLJs on the marine ecological environment is still unclear. We used multi-satellite observation data and meteorological reanalysis datasets to study the potential impact of LLJs on the marine biophysical environment over the Beibuwan Gulf (BBG) in summer during 2015–2019. In terms of the summer average, the sea surface wind vectors on LLJ days became stronger in the southwesterly direction relative to those on non-LLJ days, resulting in enhanced Ekman pumping (the maximum upwelling exceeds 10 × 10–6 m s–1) in most areas of the BBG, accompanied by stronger photosynthetically active radiation (increased by about 20 μmol m–2 s–1) and less precipitation (decreased by about 3 mm day–1). These LLJ-induced hydrometeorological changes led to an increase of about 0.3 °C in the nearshore sea surface temperature and an increase of 0.1–0.5 mg m−3 (decrease of 0.1–0.3 mg m−3) in the chlorophyll-a (chl-a) concentrations in nearshore (offshore) regions. Intraseasonal and diurnal changes in the incidence and intensity of LLJs potentially resulted in changes in the biophysical ocean environment in nearshore regions on intraseasonal and semi-diurnal timescales. The semi-diurnal peak and amplitude of chl-a concentrations on LLJ days increased with respect to those on non-LLJ days. Relative to the southern BBG, LLJ events exhibit greater impacts on the northern BBG, causing increases of the semi-diurnal peak and amplitude with 1.5 mg m−3 and 0.7 mg m−3, respectively. This work provides scientific evidence for understanding the potential mechanism of synoptic-scale changes in the marine ecological environment in marginal seas with frequent LLJ days.

The meteorological causes of winter droughts over Southwest China (SWC) have been widely investigated in recent years; however, little information is available on the impact of these droughts on air pollution. This study (1) characterized and quantified the impact of winter droughts on air pollution over SWC and (2) investigated the atmospheric teleconnections associated with the winter droughts in this region using air pollution monitoring data, routinely observed meteorological data, and National Centers for Environmental Prediction/National Center for Atmospheric Research and ERA-Interim reanalysis data. The main results are as follows: (1) A surface high pressure system together with a weak descent in the middle troposphere was the main cause of the SWC drought in December 2017. (2) It has been found that precipitation, the number of precipitation days and the atmospheric boundary layer height all decreased during the droughts, resulting in unfavorable conditions for the dispersion of air pollutants. (3) The concentrations of PM2.5 and PM10 were much higher during dry periods than those during non-dry periods over SWC, especially in the Sichuan Basin of the SWC. (4) WRF-Chem simulations reproduced the observed changes in air pollutant concentrations between dry and non-dry conditions. (5) Atmospheric teleconnections associated with the winter droughts in SWC were negative phases of the conventional Eurasian teleconnection, Eastern Atlantic/Western Russia pattern, Arctic Oscillation and North Atlantic Oscillation, and a La Nina event. Overall, this study provides scientific support for the long-term potential and accurate short-term predictions of air pollution in SWC.

In recent years, surface ozone (O3) pollution during summertime (June–August) over eastern China has become more serious, and it is even the case that surface O3 and PM2.5 (particulate matter with aerodynamic diameter ≤ 2.5 μm in the air) pollution can co-occur. However, the synoptic circulation pattern related to this compound pollution remains unclear. In this study, the T-mode principal component analysis method is used to objectively classify four synoptic weather patterns (SWPs) that occur over eastern China, based on the geopotential heights at 500 hPa during summertime from 2015 to 2018. Four SWPs of eastern China are closely related to the western Pacific subtropical high (WPSH), exhibiting, significant intraseasonal and interannual variations. Note that remarkable spatial and temporal disparities of surface O3 and PM2.5 pollution are given under these four different SWPs according to the ground-level air quality and meteorological observations. In areas controlled by the WPSH or the prevailing westerlies, O3 pollution is mainly caused by photochemical reactions of nitrogen oxides and volatile organic compounds under weather conditions of high temperature, moderate humidity and slight precipitation. In particular, the warm moist flow brought by the WPSH can promote hygroscopic growth of fine particulate matter in some local areas, resulting in the increase of PM2.5 concentrations, which may form co-occurring surface O3 and PM2.5 pollution. In addition, the low boundary layer height and frequency of light-wind days are closely related to the transmission and diffusion of pollutants under the different SWPs, modulating the levels of O3–PM2.5 compound pollution. Overall, our findings demonstrate the different roles played by synoptic weather patterns in driving regional surface O3–PM2.5 compound pollution, in addition to the large quantities of emissions, and may also provide insights into the regional co-occurring high PM2.5 and high O3 level via the effects of certain meteorological factors.

Accurate estimation of the spatiotemporal variations of solar radiation is crucial for assessing and utilizing solar energy, one of the fastest‐growing and most important clean and renewable resources. Based on observations from 2,379 meteorological stations along with scare solar radiation observations, the random forest (RF) model is employed to construct a high‐density network of daily global solar radiation (DGSR) and its spatiotemporal variations in China. The RF‐estimated DGSR is in good agreement with site observations across China, with an overall correlation coefficient (R) of 0.95, root‐mean‐square error of 2.34 MJ/m2, and mean bias of −0.04 MJ/m2. The geographical distributions of R values, root‐mean‐square error, and mean bias values indicate that the RF model has high predictive performance in estimating DGSR under different climatic and geographic conditions across China. The RF model further reveals that daily sunshine duration, daily maximum land surface temperature, and day of year play dominant roles in determining DGSR across China. In addition, compared with other models, the RF model exhibits a more accurate estimation performance for DGSR. Using the RF model framework at the national scale allows the establishment of a high‐resolution DGSR network, which can not only be used to effectively evaluate the long‐term change in solar radiation but also serve as a potential resource to rationally and continually utilize solar energy.

Heavy PM2.5 (particulate matter with aerodynamic diameter equal to or less than 2.5 mu m) pollution and urban heat island (UHI) pose increasing threats to human health and living environment in populated cities. However, how PM2.5 pollution affects the UHI intensity (UHII) has not been fully understood. The impacts of PM2.5 on the wintertime UHII in the Beijing-Tianjin-Hebei megalopolis of China are explored during 2013-2017. The results show that the UHII at the time of daily maximum/minimum temperature (UHIImax/UHIImin) exhibits a decreasing/increasing tendency as PM2.5 concentration increases, causing a continuous decrease in the diurnal temperature range. These effects are mediated via aerosol-radiation interaction (aerosol-cloud interaction) under clear-sky (cloudy) condition. The changes in PM2.5 concentration further cause different relative trends of UHII(ma)x/UHIImin/diurnal temperature range across different cities in the Beijing-Tianjin-Hebei region, which are likely related to the differences in both the PM2.5 composition and city size. This study provides insights on how air pollution affects urban climate and would help to design effective mitigation strategies. Plain Language Summary A detailed understanding of the relationship between PM2.5 (particulate matter with aerodynamic diameter equal to or less than 2.5 mu m) and the urban heat island (UHI) effect is significant for climate change adaption, planning, and sustainable development in urban regions. While the Beijing-Tianjin-Hebei (BTH) megalopolis of China is among the areas with the highest population densities and fastest urbanization rates in the world, the impacts of PM2.5 pollution on UHI, along with their regional differences in the BTH megalopolis, remain unclear. This study demonstrates that different PM2.5 concentrations in the BTH region pose various influences on the UHI intensities and their change rates in different cities of varying sizes. The UHI intensities during daytime and nighttime, respectively, exhibit weakening and strengthening tendency as PM2.5 concentration increases. These effects are mediated via aerosol-radiation interaction under clear-sky condition and aerosol-cloud interaction in cloudy weather. The relative changes in the UHI magnitudes were mainly determined by PM2.5 composition and city size. The asymmetrical influences of PM2.5 on the daytime and nighttime UHI intensities caused continuous decreases in the diurnal temperature ranges in the urban areas as the pollution level increased. Our study improves the understanding of urban climate affected by air pollution and provides a scientific basis for the mitigation of UHI impacts.

Although numerous studies have been conducted around the world to investigate the meteorological causes of and disasters due to cold-air outbreaks, the effects of these events on air pollution have received little attention. This study quantitatively investigated the purification of air pollution by a super strong cold-air outbreak along with cold front movement from the north to the south of the Chinese mainland in January 2016 using routinely observed meteorological data, air pollution monitoring data, and NCEP/NCAR and ERA-Interim reanalysis data. Some of the main results are as follows: (1) There were strong decreases in the concentrations of the five studied air pollutants in most parts of the Chinese mainland during the cold frontal passage. Spatially, the regions with the largest decreases in air pollutant concentrations were consistent with those with negative anomalous centers of 24-h surface air temperature (SAT) changes and positive anomalous centers of 24-h sea level pressure (SLP) changes. These findings provide a new reference for air quality forecasts in the Chinese mainland. (2) During the cold frontal passage, near-ground wind speed increased extensively due to downward momentum transportation and isallobaric wind, the atmospheric stratification became unstable, the atmospheric boundary layer (ABL) height was significantly uplifted, and the mean maximum mixing depth (MMD) greatly increased. These changes generated a wide-range improvement in air quality for a large area of the Chinese mainland. (3) Wind speed was identified as the most important meteorological parameter affecting the diffusion of pollutants in the absence of precipitation and snow. Variations of air pollutant concentrations (y) with wind speed (x) were fitted with a negative exponential function of y = a × e−bx. (4) The clearance ratios (CRs) of the five air pollutants by the cold front differed during the cold-air outbreak. Of these, the CR of PM2.5 was the highest, reaching 85%. Overall, the cold-air outbreak greatly contributed to improving air quality in most parts of the Chinese mainland. This shows that cold front activity is one of the most important meteorological factors to be considered to improve air quality forecasts.

We investigated the factors influencing the daily maximum boundary layer height (hmax) and their relationship with air quality in the Sichuan Basin, China. We analyzed the factors influencing hmax on cloudy and sunny days in winter using five years of observational data and a reanalysis dataset and investigated the relationship between hmax and air quality. The inversion layer in the lower troposphere has a critical impact on hmax on cloudy days. By contrast, the sensible heat flux and wind shear are the main influencing factors on sunny days, although the contribution of the sensible heat flux to hmax is less than that of the wind shear. This is because the turbulence is mainly affected by mechanical mixing induced by the topographic effect of the Tibetan Plateau to the west of the Sichuan Basin. The secondary circulation over the Sichuan Basin is weaker on cloudy days than on sunny days. These results are important for understanding the dispersion of air pollutants over the Sichuan Basin.

Sichuan Basin, located in southwest China, has been ranked as the fourth of heavily air polluted regions in China partly due to its deep mountain-basin topography. However, spatial-temporal distribution of air pollution over the basin is still unclear due to the lack of monitoring data and poor knowledge. Since January 2015, six criteria air pollutants began to be monitored in 20 cities across the basin. The measured data enable us to analyze the basin-wide spatial-temporal distribution characteristics of these air pollutants. Results revealed heavy air pollution in the bottom zone, medium in the slope zone, and light pollution in the edge zone of the Basin in terms of the altitudes of air quality monitoring stations across the Basin. The average concentrations of PM2.5 and PM10 were 55.87 μg/m3 and 86.49 μg/m3 in the bottom, 33.76 μg/m3 and 63.33 μg/m3 in the slope, and 19.71 μg/m3 and 35.06 μg/m3 in the edge, respectively. In the bottom and slope of the basin, high PM2.5 concentration events occurred most frequently in winter. While in summer, ozone became primary pollutant. Among the six air pollutants, concentrations of PM2.5 and PM10 decrease dramatically with increasing altitude which was fitted by a nonlinear relationship between particulate matter (PM) concentrations and altitude. This relationship was validated by extinction coefficient profiles from CALIPSO observations and EV-lidar data, and hence used to reflect vertical distribution of air PM concentrations. It has been found that the thickness of higher PM concentrations is less than 500 m in the basin. In the bottom of the basin, PM concentrations exhibited stronger horizontal homogeneities as compared with those in the North China Plain and Yangtze River Delta. However, gaseous pollutants seemed not to show clear relationships between their concentrations and altitudes in the basin. Their horizontal homogeneities were less significant compared to PM.

The spatial-temporal variations of the ground-based and satellite-derived PM2.5 are crucial for studying air quality, human health, and climate change. However, the existing ground-based PM2.5 monitoring network has sparsely-distributed sites and satellite cannot give 24-h PM2.5, which make it difficult to grasp the spatial and sub-daily variation characteristics of PM2.5. This study aims to fill that gap by establishing a virtual network of hourly PM2.5 concentration using the LightGBM model, based on the high-density ground meteorological observations at ~2400 sites across China. The virtual network shows a desirable performance of hourly PM2.5 estimation across China, with R2 of 0.86, root-mean-square error values of 14.99 μg/m3, and mean absolute error of 9.48 μg/m3 (the results of Cross-Validation). It also exhibits high spatial-temporal consistencies with the observed PM2.5. Spatially, the heaviest PM2.5 pollution is mainly distributed in eastern China (especially the Beijing-Tianjin-Hebei, the Yangtze and Pearl river deltas, and the Sichuan-Chongqing areas). Temporarily, PM2.5 exhibits remarkable seasonal and diurnal changes characterized by higher concentration in winter and nighttime and lower in summer and daytime. Meanwhile, we found that visibility can be used as the primary predictor in the machine learning model to enhance the accuracy of estimated PM2.5. The established virtual hourly PM2.5 network (~2400 stations) provides a more intuitive and detailed PM2.5 data for us to understand the diurnal variation of PM2.5 and monitor inter-regional transport of haze over China. It thus is of benefit to the study of air pollution control and related diseases.

Unprecedented urbanization in China facilitates the rapid development of urban agglomerations (UAs) and may exert prominent effects on regional climate and environment change. By analyzing a set of 27 extreme temperature and precipitation indices, this study examines the changes in extreme climate events in 20 UAs in China and evaluates the urbanization effects using a dynamic classification of urban and rural stations by time-varying land use/cover maps. The regional differences of the urbanization effects on extreme climate events are also investigated by a k-means clustering. It is found that, for both temperature and precipitation extremes, the urban and rural areas exhibit remarkably distinct changes and demonstrate significant urbanization effect, which also varies across different climate backgrounds. Urbanization profoundly contributes to increasing hot extremes and reducing cold extremes in most UAs, while it seems to pose the opposite effects in several UAs of arid and high-latitude regions. On average, the urbanization effect accounts for around 30% of the total change in extreme temperature events over the urban core areas of 20 UAs. On the other hand, the urbanization effects on extreme precipitation indices display stronger regional discrepancies than temperature extremes. Urbanization tends to have weakening effects on extreme precipitation events in UAs over coastal regions and intensifying influences on those in central/west China. It causes more (less) frequent and more (less) intense precipitation in UAs of inland central/west (coastal) areas. Our findings provide a systematic understanding of the urbanization effects on extreme climate and may have important implications for the mitigation of urban disasters.

Ozone (O3) pollution in China tends to become increasingly severe despite recent emission reductions. O3 is sensitive to atmospheric conditions, but its modulations by changing synoptic systems and climate are unclear, especially in mountain-basin areas such as the Sichuan Basin (SCB). This study examines the impacts of typical synoptic systems and their secular changes on O3 pollution and its health risks in mountain-basin areas, by taking SCB as an example. Seven dominant synoptic patterns are identified and three typical synoptic patterns with high- (low-) pressure are associated with high (low) O3 concentrations over entire SCB, while the pattern with uniform-pressure is accompanied by heavy (light) pollution in western (eastern) SCB. Under the synoptic patterns with high-pressure, clear weather with fewer clouds, strong solar radiation, weak convection, and hot and dry atmosphere enhances the photochemical reactions of O3 precursors and weakens the vertical mixing of O3 and its precursors, thus favoring heavy O3 pollution and posing higher health risks; whereas, the synoptic patterns with low-pressure induce the opposite changes in O3 concentration and health risks. The uniform-pressure pattern is accompanied by district spatial variations of O3 via favoring O3-related physical and chemical processes in eastern SCB and inhibiting that in the west. Under climatic changes, increasing synoptic pattern with high-pressure and decreasing synoptic pattern with low-pressure over SCB significantly increased O3 concentration and the resultant health risks over the past 40 years. Our findings provide scientific evidence from synoptic and climatic views for forecasting O3 and its health risks and for mitigating O3 pollution in mountain-basin areas.

Compared with interdecadal, interannual, or seasonal scales, the variations of diurnal temperature range (DTR) at the intraseasonal scale and their driving forces are less understood. Using surface meteorological observations and multi-source satellite retrievals during 2013-2017, together with Random Forest modeling, this study examines the intraseasonal variation of summer DTR in the Yangtze River Delta (YRD) region, China, and determines its potential driving factors [i.e., daily maximum/minimum surface air temperature (SATmax/SATmin), sunshine duration (SSD), rainfall, altitude, land vegetation cover, and land surface thermal environment including daytime/nighttime land surface temperature (LSTD/LSTN) and anthropogenic heat flux (AHF)]. It is found that the intraseasonal variation of DTR at both 8-day and monthly scales in the YRD exhibits regional differences and is modulated by different primary factors across the region. The evident intraseasonal variation of DTR, with a peak in June, in the northern YRD, is largely attributable to nighttime temperatures (SATmin and LSTN), which in turn are mainly attributable to different LSTN responses to the underlying surface cover changes associated with crop rotation. In contrast, as the YRD metropolitan area (MYRD) is covered by a large proportion of built-up surfaces, and the weather stations there are surrounded by a higher surface thermal environment and AHF, the MYRD has stably higher LST and SATmin in the whole summer season. Thus, the summer DTR in the MYRD exhibits marginal intraseasonal variations. In the southern YRD, there is also a distinct DTR characteristic, with a maximum in July and minimum in June, since this region is largely covered by forests with constantly high-density vegetation cover, and its DTR variation is mainly forced by SSD, which directly affects SATmax. The findings reported here have important implications for understanding the influences of human activities on regional climate and environmental change for other regions of the world that experience various external forcings.

Arid regions suffer seriously from frequent and severe heatwaves. As compared to other regions, however, the synoptic-scale characteristics and mechanism of heatwave events in arid region are less understood and warrant in-depth investigations. Using observational and reanalysis datasets, here we investigate the heatwave behaviors in arid northwest China (ANC) during 1961–2014. Results reveal that heatwaves in ANC are accompanied by a hot and dry air column. In particular, the main synoptic systems are characterized by a warm-core low pressure in the lower troposphere and a deep warm high pressure in the middle and upper troposphere over the ANC and nearby regions, resulting in the heatwaves there. In the lower troposphere, the prevailing southerly flow induced by the warm-core low pressure increases the near-surface air temperature via warm advection. In the middle and upper troposphere, the deep warm high pressure is accompanied by sinking motion over the ANC surrounding region, leading to clear-sky conditions and less precipitation. These pressure configurations facilitate temperature increases by radiative heating and subsidence warming. Notably, the deep warm high pressure is closely associated with the evolution and eastward movement of a wave-like pattern over the northern midlatitudes. Over the long-term period, the heatwave in ANC exhibits significant intensifying trends in terms of increasing frequency (0.40 events decade−1), prolonging duration (1.67 days decade−1), and strengthening amplitude (0.32 °C decade−1). Also, we find that heatwaves in ANC tend to commence earlier and end later during recent decades. These findings provide scientific evidence from the synoptic and climatic perspectives for understanding the formation mechanism and forecasting of the heatwaves in arid regions.

It is known that rapid expansion in urban area exerts prominent effects on surface or near-surface temperature and the urban heat island effect. However, whether and how urban land expansion influences atmospheric humidity, a key indicator closely relating to human comfort and urban environment, warrant further investigations. By taking Guangdong Province as an example, in this study we examined the change in atmospheric moisture over South China during 1961–2014 and evaluated the urban expansion effects. We found a prominent drying tendency over Guangdong, as characterized by decreasing relative and specific humidities and increasing vapor pressure deficit. The drying trend has a positive correlation with urban expansion speed and exhibited regional differences, with particularly stronger trends in more urbanized and populated areas such as the Pearl River Delta. Using a dynamic classification of urban / rural stations based on time-varying high-resolution land use / land cover maps, we estimated that urbanization contributed to approximately 50% of the total drying trend in the urban areas of Guangdong. Possible mechanisms and processes underlying the urban expansion effect were also discussed. We suggested that the changes in atmospheric moisture and the urban expansion effect should be considered in future climate change mitigation, urban planning, and landscape design to support sustainable development in urban areas.

To assess the health risk of PM2.5, it is necessary to accurately estimate the actual exposure level of the population to PM2.5. However, the spatial distribution of PM2.5 may be inconsistent with that of the population, making it necessary for a high-spatial-resolution and refined assessment of the population exposure to air pollution. This study takes the Yangtze River Delta (YRD) Region as an example since it has a high-density population and a high pollution level. The brightness reflectance of night-time light, and MODIS-based (Moderate Resolution Imaging Spectroradiometer) vegetation index, elevation, and slope information are used as independent variables to construct a random-forest (RF) model for the estimation of the population spatial distribution, before any combination with the PM2.5 data retrieved from MODIS. This enables assessment of the population exposure to PM2.5 (i.e., intensity of population exposure to PM2.5 and population-weighted PM2.5 concentration) at a 3-km resolution, using the year 2013 as an example. Results show that the variance explained for the RF-model-estimated population density reaches over 80%, while the estimated errors in half of counties are < 20%, indicating the high accuracy of the estimated population. The spatial distribution of population exposure to PM2.5 exhibits an obvious urban−suburban−rural difference consistent with the population distribution but inconsistent with the PM2.5 concentration. High and low PM2.5 concentrations are mainly distributed in the northern and southern YRD Region, respectively, with the mean proportions of the population exposed to PM2.5 concentrations > 35μg/m3 close to 100% in all four seasons. A high-level population exposure to PM2.5 is mainly found in Shanghai, most of the Jiangsu Province, the central Anhui Province, and some coastal cities of the Zhejiang Province. The highest risk of population exposure to PM2.5 occurs in winter, followed by spring and autumn, and the lowest in summer, consistent with the PM2.5 seasonal variation. Seasonal-averaged population-weighted PM2.5 concentrations are different from PM2.5 concentrations in the region, which are closely related to the urban-exposed population density and pollution levels. This work provides a novel assessment of the proposed population-density exposure to PM2.5 by using multi-satellite retrievals to determine the high-spatial-resolution risk of air pollution and detailed regional differences in the population exposure to PM2.5.

To evaluate the distribution characteristics of atmospheric pollutants in Lanzhou City and their health-effects on daily respiratory disease hospital admissions.Using the same period atmospheric pollutants (PM10, SO2 and NO2) data, meteorological data and daily respiratory disease hospital admissions data, from 2001 to 2009 in Lanzhou, to analysis of the distribution of three air pollutants(PM10, SO2 and NO2), and their correlation with meteorological factors. On this basis, a time series semi-parametric generalized additive model (GAM) was used to analyze the exposure-effect relationship between air pollution and daily respiratory disease hospital admissions.The concentrations of PM10 and SO2 showed a decreased trends during the study period of 9 years, and NO2 showed weakly fluctuations and remained substantially unchanged. The highest concentrations of the three pollutants were presented in the winter and the lowest value were presented in the summer. PM10 concentration monthly distribution was bimodal distribution, SO2 and NO2 were unimodal distribution. There were significantly correlation between meteorological factors and pollutants. The results showed that there was certain lag effect of three kinds of air pollutants on daily respiratory disease hospital admissions. The lag time of three air pollutants all were 0 - 6 day moving average (avg06). An increase of 10 µg/m in PM10, SO2 and NO2 were significantly associated with the excess risk (ER) of 0. 45%, 1. 35% and 3. 02% for all respiratory hospital admissions.The ambient NO2, SO2 and PM10 concentrations have an adverse effect on daily hospital admissions for respiratory diseases of residents in Lanzhou City.