Search

Water-use efficiency (WUE) is an important indicator for understanding the coupling of carbon and water cycles in terrestrial ecosystems. It provides a comprehensive reflection of ecosystems' responses to various environmental factors, making it essential for understanding how ecosystems adapt to complex environmental changes. Using satellite-based estimates of gross primary productivity (GPP) and evapotranspiration (ET), our study investigated the spatiotemporal variations in WUE across China's terrestrial ecosystems from 2001 to 2020. We employed the geographic detector method, partial correlation analysis, and ridge regression to assess the contributions of different factors (temperature, precipitation, solar radiation, vapor pressure deficit, leaf area index, and soil moisture) to GPP, ET, and WUE. The results show significant increases in GPP, ET, and WUE during the study period, with increase rates of 6.70 g C m−2 yr−1, 2.68 kg H2O m−2 yr−1, and 0.007 g C H2O m−2 yr−1, respectively. More than three-quarters of the regions with significant trends in WUE (p < 0.05) displayed notable increases in WUE (p < 0.05). Among all driving factors, leaf area index (LAI) made the largest contribution to WUE, particularly in warm temperate semi-humid regions. Precipitation and solar radiation were the primary climatic influences in arid regions of northern China and humid regions of southwestern China, respectively. [ABSTRACT FROM AUTHOR]

[Objective] The aims of this study are to understand the spatiotemporal trends in vegetation photosynthetic phenology (SOS) in spring, to reveal the contribution of climate factors, and to provide a scientific basis for increasing carbon sequestration capacity of vegetation. [Methods] Northern China was taken as a case study. The changing characteristics of SOS from 2001 to 2020 in this region were analyzed based on the gross primary productivity data from solar-induced chlorophyll fluorescence (GOSIF-GPP) and meteorological data. Furthermore, the responses of different climate factors (e. g., minimum air temperature, maximum air temperature, precipitation, and solar radiation) on SOS were assessed. [Results] The mean value of SOS in northern China mainly occurred from the 105th day to the 150th day in a year, and the later SOS was mainly distributed in Qinghai and east-central Inner Mongolia. The regional mean SOS from 2001 to 2020 showed a significant advancing trend (p＜0.05) with a magnitude of-0.31 d/a, and the SOS of all vegetation types showed a significant advancing trend (p＜0.05). The SOS was negatively sensitive to four climate factors, especially the maximum and minimum air temperatures were important climate factors influencing the SOS changes, reflecting that warmer temperature was favorable to the advancing SOS in the most region of northern China. [Conclusion] The SOS changes in the northern China are heterogeneous, and air temperature is the main factor affecting SOS changes. [ABSTRACT FROM AUTHOR]

Copyright of Remote Sensing for Natural Resources is the property of Remote Sensing for Natural Resources Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

An efficacious strategy to adapt to climate change involves optimizing the planting season, a technique that has been extensively utilized to enhance the use of solar radiation and temperature resources in rice cultivation. Field experiments were executed in the middle and lower reaches of the Yangtze River, China, employing three distinct rice cultivars and seven disparate sowing periods spanning 2019 to 2021. The objective of assessing the impact of sowing date on apparent radiation use efficiency (RUEA), accumulated temperature use efficiencies (TUE), and overall rice yield. Subsequent to the delay of sowing dates, the duration of the comprehensive growth period initially exhibited a declining trajectory before subsequently escalating, with the reduction predominantly ascribed to a decrease in the number of days preceding heading. Furthermore, there was a tendency for both the mean daily and effective cumulative solar radiation to decline over the course of the growing period. The yield of the three rice varieties demonstrated an initial surge, which was then followed by a subsequent decline in reaction to the delay of sowing dates. A correlation analysis disclosed that solar radiation and effective cumulative temperature (EAT) were the predominant elements impacting grain yield. The outcomes of the path analysis indicate that EAT exerts the most substantial influence on yield, succeeded by cumulative total solar radiation (TSR), while photothermal quotient (PTQ) demonstrates the least impact on yield. There was a significant positive correlation between EAT and cumulative TSR with spikelets per panicle (0.237** and 0.218**), grain filling (0.753** and 0.576**), and grain weight (0.339** and 0.359**), respectively. The findings of this study indicate that an increase in yield is facilitated when the EAT after heading exceeds 594.9 ℃, the EAT surpasses 2016.7 ℃, the cumulative TSR before heading is above 1548.7 MJ m− 2, the cumulative TSR after heading is over 603.0 MJ m− 2, and the cumulative total radiation throughout the entire growth period is more than 2151.8 MJ m− 2. Furthermore, the most optimal sowing date, as identified by this study, is June 6. This study provides key insights into boosting rice productivity in the middle and lower reaches of the Yangtze River, China by analyzing the impact of temperature and solar radiation on yield and identifying optimal growth conditions. Clinical trial number Not applicable. [ABSTRACT FROM AUTHOR]

Taking the South China karst as an example, this study constructed compound use efficiency (COM) through water, light, and carbon use efficiency (WUE, LUE, and CUE), analysed the divergence characteristics of COM, and clarified the dominant factors of COM variation and their sensitivity. It showed that from 2000 to 2018, WUE, LUE, CUE, and COM showed significant non-linear characteristics, spatially, which were distributed in a "northeast–southwest" pattern. Different factors on COM exhibited significant spatial heterogeneity. The COM first stabilized and then exhibited a fluctuating decrease with rising elevation and slope. The COM increased sharply and then slightly decreased as the temperature and precipitation increased but decreased with enhanced solar radiation and human activity intensity (HAI). HAI had the greatest effect on COM change, and the COM-HAI sensitivity first decreased and then increased; 2006 was the most abrupt year, and this may be related to the time-lagged effect of ecological restoration on vegetation. [ABSTRACT FROM AUTHOR]

Absorbing aerosols can absorb solar radiation, affect the atmospheric radiation balance, and further have a profound influence on the global and regional climates. The absorption aerosol optical depth (AAOD) as well as the absorption Angstrom exponent (AAE) across China over 2005–2018 were systematically studied through the Ozone Monitoring Instrument (OMI) dataset. The monthly AAOD samples from the OMI generally showed a good correlation (~0.55) compared to the monthly data from AERONET at four typical sites (North: Xianghe, East: Taihu, South: Hongkong Polytechnic Univ; Northwest: Sacol) across China. The ensemble annual average of the OMI AAOD at 388 and 500 nm is 0.046 and 0.022, with minor changes during 2005–2015, and a relatively fast increase after that. The winter and spring seasons depict the maximum mean AAODs, followed by autumn, whereas summer shows minimum levels. On the contrary, the high AAE values appear in summer and low values in winter. The order of the annual average AAOD500 from 2005 to 2018 is the Tarim Basin (TB, 0.041) > the Yellow River Basin (YRB, 0.023) > Beijing and Tianjin (BT, 0.026) > the Sichuan Basin (SB, 0.023) > Nanjing and Shanghai (NS, 0.021) > the Pearl River Delta (PRD, 0.017), whereas the AAE388–500 exhibits the opposite trend except for the TB (3.058). From 2005 to 2018, the AAOD rises by nearly 1.5–2.0 fold in the six typical regions, implying a severe situation of dust and/or BC aerosol pollution in the last several years. The monthly mean AAOD388 over the TB, the SB, the YRB, BT, the PRD, and NS is estimated to be smallest at 0.072, 0.024, 0.026, and 0.027 in July, 0.024 in June, and 0.025 in September, respectively, whilst largest in January for NS, the YRB and BT, April for the TB, February for the SB, and March for the PRD with 0.055, 0.077 and 0.067, 0.123, and 0.073 and 0.075, respectively. The monthly averaged AAOD500 in each region is consistently about half of the AAOD388. The highest AAE appears in June while the lowest values are in December and January, and the daily AAE values in episode days slightly decrease as compared to non-episode days. Our study indicates that northwestern China plays an important role in the overall AAOD as a result of dust aerosols stemming from desert areas. Moreover, the meteorological conditions in winter and early spring are associated with more energy consumption conducive to the accumulation of high black carbon (BC) aerosol pollution, causing high alert levels of AAOD from November to the following March. [ABSTRACT FROM AUTHOR]

The accumulation processes of loess in northeastern (NE) China record the varying characteristics of the East Asian Monsoons (EAM) and the evolution of the local environment. In this study, grain size end-member analysis of the Dajiugang (DJG) section deposited since the Last Glacial in Chifeng City, NE China was used to reveal the dynamic depositional characteristics of loess and environmental evolution of NE China. Results showed that the Chifeng loess comprises three grain size end-members (EM), EM1 (7.38 µm), EM2 (49.4 µm) and EM3 (90.00 µm), indicating the three transport dynamics of dust delivered to the region. The EM1 represents atmospheric dust as the background. The EM2, mainly derived from relatively distant-sources deposition, and EM3, material transported over a short distance, correspond to the intensity of southwesterly winds and the East Asian Winter Monsoon (EAWM), respectively. Results of climate reconstruction, combined with other records, showed that the climate was generally cold and dry with a strong EAWM and a weak East Asian summer monsoon (EASM) during the stadial-period [Marine Isotope Stage (MIS) 4 and MIS 2 stage]. The climate was predominantly warm and humid, with weak EAWM and surface winds and strong EASM in interstadial- period (MIS 3 and MIS 1 stage). Comparisons between indicators, e.g., EM3, the LR04 benthic δ18O stack and summer insolation at 65°N suggest that the strong EAWM is induced by increasing the Northern Hemisphere ice volume and reduced summer solar radiation. [ABSTRACT FROM AUTHOR]

Diffuse solar radiation (DSR) plays a critical role in renewable energy utilization and efficient agricultural production. However, there is a scarcity of high-precision, long-term, and spatially continuous datasets for DSR in the world, and particularly in China. To address this gap, a 41-year (1982–2022) daily diffuse solar radiation dataset (CHDSR) is constructed with a spatial resolution of 10 km, based on a new ensemble model that combines the clear-sky irradiance estimated by the REST2 model and a machine-learning technique using precise cloud information derived from reanalysis data. Validation against ground-based measurements indicates strong performance of the new hybrid model, with a correlation coefficient, root mean square error and mean bias error (MBE) of 0.94, 13.9 W m−2 and −0.49 W m−2, respectively. The CHDSR dataset shows good spatial and temporal continuity over the time horizon from 1982 to 2022, with a multi-year mean value of 74.51 W m−2. This dataset is now freely available on figshare to the potential benefit of any analytical work in solar energy, agriculture, climate change, etc (https://doi.org/10.6084/m9.figshare.21763223.v3). [ABSTRACT FROM AUTHOR]

Net precipitation (NP) is the primary source of soil water essential for the functioning of vegetated ecosystems. By quantifying NP as the difference between gross precipitation and canopy interception evaporation, this study examined the dynamics of NP in China from 2001 to 2020 and the contribution of environmental factors to NP variations was investigated. The findings revealed a multiyear mean NP of 674.62 mm, showcasing a 2.93 mm/yr increase. The spatiotemporal variations in NP were mainly attributed to a remarkable increase in precipitation rather than canopy interception. Notably, climate (temperature, wind speed, surface solar radiation downward and vapor pressure deficit) and vegetation factors (leaf area index and net primary productivity) played a dominant role in NP in 61.53% and 15.39% of China, respectively. The dominant factors contributing to NP changes were vapor pressure deficit (mean contribution rate: −43.68%), temperature (mean contribution rate: 11.69%), and leaf area index (mean contribution rate: 2.13%). The vapor pressure deficit negatively exerts a negative influence on the southern and eastern regions. Temperature and leaf area index have the greatest effect on the northeastern and southwestern regions, respectively. The results provide valuable insights into the pivotal role of climatic and vegetation factors in ecohydrological cycles. [ABSTRACT FROM AUTHOR]

Evidence of climate change can be observed in multiple climate variables, including air temperature rises and precipitation pattern changes. To manage water resources and agriculture effectively, it's important to project climate variables' changes at the local level, as these changes can vary depending on the specific area. The baseline weather data trend was analyzed using the percentage change (PC) method and the Innovative Trend Analysis (ITA) technique at three cluster levels: cluster 1 (PC: 0–20%), cluster 2 (PC: 21–80%), and cluster 3 (PC: 81–100%). The precipitation (Prec.), maximum (Tmax), and minimum (Tmin) temperatures showed downward trends in 9, 4, and 6 stations out of 24 stations, respectively. The SDSM model performed best in predicting Prec., while the LARS-WG model was more effective in predicting Tmax, Tmin, and solar radiation (SR). The average monthly Prec. percentage change shows both rising and falling trends in different weather areas for all three time periods (2040, 2060, and 2090) and for both RCPs (RCP4.5 and RCP8.5). In contrast to precipitation, both Tmax and Tmin consistently showed an upward trend across all meteorological stations for both RCPs and three-time frames. Across the four distinct plain regions, the overall projection suggests a slight increase in precipitation. The study predicts the highest increase in precipitation to occur in June across all meteorological stations. Seasonally, the greatest increase in precipitation is projected during summer (JJA) by 5.10%, while the largest decrease is expected during winter (DJF) by 3.29%. Additionally, precipitation variability shows an increase from RCP4.5 to RCP8.5 and from near-term (2040) to long-term (2090), with the northern Jiangsu Plain exhibiting the highest variation. The biggest rise in Tmax/Tmin was observed at RCP 8.5, by 2.69/2.39 °C, and in the long term (2090), by 3.25/2.86 °C. This was compared to RCP 4.5 by 1.73/1.51 °C, in the near term (2040) by 1.24/1.08 °C, and in the mid-term (2060) by 2.14/1.90 °C. The highest increase in Tmax is expected compared to Tmin, leading to the highest diurnal temperature (DTR) at all three periods and both RCPs. Seasonally, the highest increase is projected in the autumn for both Tmax and Tmin. Similar to Tmax and Tmin, the longest time period (2090) exhibits the highest increase in solar radiation, followed by the midterm (2060) and then the short term (2040). Unlike Tmax and Tmin, the highest increase in SR is predicted during the summer season (JJA), while the lowest increase is projected during the winter season (DJF). The future projections highlight the expectation of a wettest and hottest summer, along with the driest and coldest winter. These findings provide valuable insights for water resource planners, agricultural managers, and policymakers, as these climate variables play a significant role in crop production and water allocation decisions. [ABSTRACT FROM AUTHOR]

Wheat crops underpin contemporary global food security. Predominant wheat production zones in China include the Huang‐Huai‐Hai‐Plain and the Mid‐Lower Reaches of the Yangtze River, although climatic effects on productive potential across these regions vary markedly in space and time. Here, we conducted field experiments during the wheat season of 2015–2018 to examine environmental effects on growth, with fertilization and irrigation provided at levels ensuring that nutrient and water stress exposure was minimal. Yields in Huang‐Huai‐Hai‐Plain and the Mid‐Lower Reaches of the Yangtze River averaged 8950 and 4818 kg ha−1, respectively. Yield variation across regions was primarily related to spike number per unit area and grain number per spike. Maturity biomass was higher in Huang‐Huai‐Hai‐Plain; this translated into higher grain yields. Lower temperature and longer growing duration between emergence and jointing in Huang‐Huai‐Hai‐Plain afforded higher tillering and spike numbers, whereas higher growth rates from jointing to maturity resulted in higher biomass production in Huang‐Huai‐Hai‐Plain compare with the Mid‐Lower Reaches of Yangtze River. Growth rate, grain numbers and yield were positively correlated with the ratio of daily intercepted solar radiation to mean temperature during jointing to anthesis, termed photothermal quotient. Collectively, our results suggest that growth rate accounted for more variation in biomass production compared with growth duration, and the photothermal conditions in the Mid‐Lower Reaches of the Yangtze River were restrictive for spike development and yield formation. Our results help disentangle drivers of crop growth through the development of agro‐environmental conceptual frameworks, enabling a better understanding of yield variability in space and time. [ABSTRACT FROM AUTHOR]

Large‐scale photovoltaic (PV) plants are growing rapidly in drylands because of the rich solar radiation and vast unutilized land. The transformation of landscapes in dryland has threatened local fragile vegetation. Existing studies have investigated the issue by field observations and satellite data, yet the spatial differences in vegetation changes due to dryland PV plants deployment and underlying driving mechanisms remain poorly understood. In this study, Landsat Normalized Difference Vegetation Index data were used to assess the vegetation changes disturbed by PV plants in China's drylands. We further identified environmental factors affecting vegetation changes based on random forest regression model. Our findings reveal the spatial heterogeneity in the impact of PV plants on vegetation dynamics—PV plant deployment promoted the growth of vegetation in the vast majority of arid and hyper‐arid drylands, while it tends to cause vegetation decline in the sub‐humid and semi‐arid drylands. The impact of PV plants on vegetation dynamics depends on local environmental conditions. We found that deploying PV plants in areas with sparse vegetation, low humidity, and long sunshine duration is more likely to promote vegetation restoration. The findings and data maps with highly detailed information can help guide solar energy operators in siting and ecological restoration to enhance techno–ecological synergies in the future. Plain Language Summary: In drylands, large‐scale photovoltaic (PV) plants are rapidly expanding. However, the spatial differences in vegetation changes caused by the PV plants deployment and underlying driving mechanisms remains understudied. Here, we used satellite remote sensing imagery to monitor the impact of PV plant deployment on vegetation dynamics in drylands. We employed machine learning models and multi‐source data sets to explore the environmental factors influencing the vegetation effects of PV plants. Our study findings indicate that the PV plants deployment has varying impacts on vegetation dynamics in different regions. In areas with sparse vegetation, low humidity, and long sunlight duration, PV plant deployment is more conducive to promoting vegetation restoration. These findings deepen our understanding of the ecological impacts of PV plants in drylands and emphasize the techno‐ecological synergistic benefits of PV plants in dryland ecosystems. Key Points: Existing PV plants exhibit spatially varied impacts on vegetation dynamicsThe impact of PV plants on vegetation dynamics depends on local environmental conditionsPV deployment helps enhance techno‐ecological synergies in most drylands of China [ABSTRACT FROM AUTHOR]

Ultraviolet (UV) radiation is closely related to health, but limited measurements hindered further investigation of its health effects in China. Machine learning algorithm has been widely used in predicting environmental factors with high accuracy, but limited studies have done for UV radiation. This study aimed to develop UV radiation prediction model based on random forest method, and predict UV radiation at daily level and 10 km resolution in mainland China in 2005–2020. A random forest model was employed to predict UV radiation by integrating ground UV radiation measurements from monitoring stations and multiple predictors, such as UV radiation data from satellite. Missing data of satellite-based UV radiation was filled by three-day moving average method. The model's performance was evaluated through multiple cross-validation (CV) methods. The overall R2 (root mean square error, RMSE) between measured and predicted UV radiation from model development and model 10-fold CV was 0.97 (15.64 W m-2) and 0.83 (37.44 W m-2) at daily level, respectively. The model with OMI EDD performed higher predicting accuracy than the one without it. Based on predictions of UV radiation at daily level and 10 km spatial resolution and nearly 100 % spatiotemporal coverage, we found UV radiation increased by 4.20 % while PM2.5 levels decreased by 48.51 % and O3 levels rose by 22.70 % in 2013–2020, suggesting a potential correlation among these environmental factors. Uneven spatial distribution of UV radiation was found to be associated with factors such as latitude, elevation, meteorological factors and seasons. The eastern areas of China posed higher risk with both high population density and UV radiation intensity. Based on machine learning algorithm, this study generated a gridded dataset characterized by relatively high precision and extensive spatiotemporal coverage of UV radiation, which demonstrates the spatiotemporal variability of UV radiation levels in China and can facilitate health-related research in the future. This dataset is currently freely available at https://doi.org/10.5281/zenodo.10884591 (Jiang et al., 2024). [ABSTRACT FROM AUTHOR]

Meteorological processes are investigated during the development of deep convective clouds over the Yellow Sea inside a quasi‐stationary rainy front in the East Asian region. WRF‐Chem simulations were conducted to assess the effects of aqueous‐phase chemistry on the concentrations of PM2.5 and hydrometers in deep convective clouds and the associated precipitation. A reduction in the south‐north air temperature gradient induced an inactive quasi‐stationary rainy front with nonlinear zonal clouds during the Changma period in the East Asian region in 2017, causing intensified surface heating due to solar radiation. A mesoscale cyclone was formed near the Shantung Peninsula inside the inactive quasi‐stationary rainy front, extending the eastward tongue of the troughs across the Yellow Sea. Deep convective clouds (CC16 on July 16, CC17 on July 17, and CC18 on July 18) formed in a severe mode on the east side of the mesoscale cyclone over the Yellow Sea at midnight as the warm type, as the warm and humid south‐southwesterly winds present at that time produced convection energy. PM2.5 from eastern China flowed into warm‐type CC16, CC17, and CC18 areas, producing more cloud droplets below 500 hPa via activation. In contrast, the release of additional condensation heat amplifies updrafts within convective clouds during efficient nighttime environmental cooling, thereby enhancing the production of cold‐type ice, reaching altitudes of up to 15 km. The WRF‐Chem PM2.5 outcomes indicate a significant increase in heavy rainfall, exceeding 10 mm hr−1, particularly in the context of the development of cold‐type convective clouds. Plain Language Summary: The regional phenomenon of summer rain, known as Changma in Korea, usually occurs with a quasi‐stationary rainy front that forms between the continental polar air mass over the East Asian region and the maritime tropical air mass over the northwest Pacific. An inactive quasi‐stationary rainy front with nonlinear zonal clouds induced by a reduction in the south‐north air temperature gradient during the Changma period led to the efficient absorption of solar radiation on the surface of the East Asian region. In addition, convective clouds have frequently developed over the Yellow Sea inside the inactive quasi‐stationary rainy front, resulting in extreme events with large amounts of short‐term precipitation in inland Korea. We found that aqueous‐phase chemistry in convective clouds led to the production of PM2.5 and nucleated cloud droplets, enhancing precipitation on 16–18 July 2017. While warm‐type convective clouds developed due to warm and humid south‐southwesterly flows over the Yellow Sea, WRF‐Chem‐simulated PM2.5 promoted cold‐type ice production to a top convection height of 15 km. The entrainment of near‐surface PM2.5 aerosols in the south‐southwesterly flows from eastern China and the secondary activations over the boundary layer clouds enhanced heavy rainfall to more than 10 mm hr−1 in cold‐type convective clouds. Key Points: A reduction in the air temperature gradient induced an inactive rainy front, forming a mesoscale cyclone over the Yellow SeaPM2.5 aerosols in eastern China flowed out to the Yellow Sea, wherein the PM2.5 inflows contributed to the development of cold‐type cloudsCold‐type clouds promoted heavy rainfall exceeding 10 mm hr−1 over the Yellow Sea and Korea [ABSTRACT FROM AUTHOR]

The ozone (O3) variations in southeast China are largely different between mountainous forest areas located inland, and lowland urban areas located near the coast. Here, we selected these two kinds of areas to compare their similarities and differences in surface O3 variability from diurnal to seasonal scales. Our results show that in comparison with the lowland urban areas (coastal areas), the mountainous forest areas (inland areas) are characterized with less human activates, lower precursor emissions, wetter and colder meteorological conditions, and denser vegetation covers. This can lead to lower chemical O3 production and higher O3 deposition rates in the inland areas. The annual mean of 8-h O3 maximum concentrations (MDA8 O3) in the inland areas are ~15 μg·m−3 (i.e. ~15%) lower than that in the coastal areas. The day-to-day variation in surface O3 in the two types of the areas is rather similar, with a correlation coefficient of 0.75 between them, suggesting similar influences on large scales, such as weather patterns, regional O3 transport, and background O3. Over 2016–2020, O3 concentrations in all the areas shows a trend of "rising and then falling", with a peak in 2017 and 2018. Daily MDA8 O3 correlates with solar radiation most in the coastal areas, while in the inland areas, it is correlated with relative humidity most. Diurnally, during the morning, O3 concentrations in the inland areas increase faster than in the coastal areas in most seasons, mainly due to a faster increase in temperature and decrease in humidity. While in the evening, O3 concentrations decrease faster in the inland areas than in the coastal areas, mostly attributable to a higher titration effect in the inland areas. Seasonally, both areas share a double-peak variation in O3 concentrations, with two peaks in spring and autumn and two valleys in summer and winter. We found that the valley in summer is related to the summer Asian monsoon that induces large-scale convections bringing local O3 upward but blocking inflow of O3 downward, while the one in winter is due to low O3 production. The coastal areas experienced more exceedance days (~30 days per year) than inland areas (~5-10 days per year), with O3 sources largely from the northeast. Overall, the similarities and differences in O3 concentrations between inland and coastal areas in southeastern China are rather unique, reflecting the collective impact of geographic-related meteorology, O3 precursor emissions, and vegetation on surface O3 concentrations. [ABSTRACT FROM AUTHOR]

With the continuous development of rural revitalization and urbanization in China, the sustainable transformation of traditional rural architecture has become increasingly important. This study takes the Thangka exhibition hall in rural Malkang, Sichuan Province, as the research object and proposes a Thangka exhibition hall architectural design centered around solar heating and aiming for near-zero energy consumption. The research method involves establishing a solar energy system model on the roof of the exhibition hall and utilizing solar angle and area calculation formulas along with simulation software to calculate the optimal installation angle and area of solar panels, with the aim of achieving indoor temperatures that meet Thangka protection requirements while achieving zero-energy heating. Preliminary results indicate that this solar-centric near-zero energy architectural design can effectively promote the increase in indoor temperature through solar thermal conversion. Additionally, through calculation and simulation, the optimal installation angle for the solar panels achieving zero-energy heating is determined to be 24.25 with an azimuth angle of −1.2. The optimum installation area for solar panels is 8.2 square meters in the showroom and 2.7 square meters in the storeroom. Among these, the solar panel area for the Thangka exhibition hall constitutes 4.12% of the total area and is required for maintaining Thangka protection temperature requirements throughout the year, while the solar panel area for the storage room constitutes 1.88% and is also needed for the same purpose. Studying the optimal installation angle and area of solar panels can transform the exhibition hall into a near-zero-energy building, meeting the temperature requirements for Thangka preservation and human thermal comfort, while also achieving optimal economic benefits. This provides guidance and a reference for promoting near-zero-energy buildings in rural areas. [ABSTRACT FROM AUTHOR]

Gross primary productivity (GPP) is a reliable measure of the carbon sink potential of terrestrial ecosystems and is an essential element of terrestrial carbon cycle research. This study employs the diffuse fraction-based two-leaf light-use efficiency (DTEC) model to imitate China's monthly GPP from 2001 to 2020. We studied the trend of GPP, investigated its relationship with climatic factors, and separated the contributions of climate change and human activities. The findings showed that the DTEC model was widely applicable in China. During the study period, China's average GPP increased significantly, by 9.77 g C m−2 yr−1 (p < 0.001). The detrimental effect of aerosol optical depth (AOD) on GPP was more widespread than that of total precipitation, temperature, and solar radiation. Areas that benefited from AOD, such as Northwest China, experienced significant increases in GPP. Climate change and human activities had a primary and positive influence on GPP during the study period, accounting for 28% and 72% of the increase, respectively. Human activities, particularly ecological restoration projects and the adoption of advanced agricultural technologies, played a significant role in China's GPP growth. China's afforestation plan was particularly notable, with the GPP increasing in afforestation areas at a rate greater than 10 g C m−2 yr−1. This research provides a theoretical foundation for the long-term management of China's terrestrial ecosystems and helps develop adaptive ecological restoration tactics. [ABSTRACT FROM AUTHOR]

Introduction: The rhizomes of Coptis plants have been used in traditional Chinese medicine over 2000 years. Due to increasing market demand, the overexploitation of wild populations, habitat degradation and indiscriminate artificial cultivation of Coptis species have severely damaged the native germplasms of species in China. Methods: Genome-wide simple-sequence repeat (SSR) markers were developed using the genomic data of C. chinensis. Population genetic diversity and structure of 345 Coptis accessions collected from 32 different populations were performed based on these SSRs. The distribution of suitable areas for three taxa in China was predicted and the effects of environmental variables on genetic diversity in relation to different population distributions were further analyzed. Results: 22 primer pairs were selected as clear, stable, and polymorphic SSR markers. These had an average of 16.41 alleles and an average polymorphism information content (PIC) value of 0.664. In the neighbor-joining (N-J) clustering analysis, the 345 individuals clustered into three groups, with C. chinensis, C. chinensis var. brevisepala and C. teeta being clearly separated. All C. chinensis accessions were further divided into four subgroups in the population structure analysis. The predicted distributions of suitable areas and the environmental variables shaping these distributions varied considerably among the three species. Discussion: Overall, the amount of solar radiation, precipitation and altitude were the most important environmental variables influencing the distribution and genetic variation of three species. The findings will provide key information to guide the conservation of genetic resources and construction of a core reserve for species. [ABSTRACT FROM AUTHOR]

Vegetation Net Primary Productivity (NPP) plays a crucial role in terrestrial carbon sinks and the global carbon cycle. Investigating the spatiotemporal dynamics and influencing factors in the Yangtze River Delta (YRD) region can furnish a solid scientific foundation for green, low-carbon, and sustainable development in China, as well as a reference for other rapidly urbanizing regions. This study focuses on the YRD region as an illustration and utilizes the Carnegie–Ames–Stanford Approach (CASA model) to quantify NPP in this region from 2000 to 2018. Investigation into the spatiotemporal dynamics and influencing factors was conducted using Theil–Sen median trend analysis and scenario analysis. The results indicate that the NPP in the YRD region from 2000 to 2018 exhibited pronounced spatial differentiation characteristics, typically exhibiting a spatial distribution pattern of being high in the south and low in the north, high in the west and low in the east. Additionally, the expansion of built-up areas and the reduction in cultivated land have the potential to reduce NPP in the YRD region. Moreover, the influence of land-use and land-cover change (LULCC) is anticipated to be relatively limited compared to that of climate change. Furthermore, changes in precipitation were found to be positively correlated with changes in NPP, with the effect being relatively more pronounced. The correlation between temperature and NPP demonstrated spatial differentiation, with a mainly positive correlation in the central and southern parts of the YRD and a mainly negative correlation in the northern part. Changes in solar radiation had a negative correlation with changes in NPP. Based on these results, it is recommended that local governments strictly enforce urban development boundaries and manage the disorderly expansion of built-up areas, enhance the regional irrigation infrastructure, and address air pollution, so as to ensure the necessary conditions for the growth of vegetation, reduce greenhouse gas emissions, and control regional temperature rises. This study can provide stronger evidence for revealing the influencing mechanisms of NPP through the control of impact conditions and the exclusion of confounding factors via scenario analysis. The policy implications can offer insights into NPP enhancement and environmental management for the YRD and other rapidly urbanizing regions. [ABSTRACT FROM AUTHOR]

Hemisphere photos are now widely applied to provide information about solar radiation dynamics, canopy structure and their contribution to biophysical processes, plant productivity and ecosystem properties. The present study aims to improve the original 'edge detection' method for binary classification between sky and canopy, which works not well for closed canopies. We supposed such inaccuracy probably is due to the influence of sky pixels on their neighbor canopy pixels. Here, we introduced a new term 'neighbor distance', defined as the distance between pixels participated in the calculation of contrast at the edges between classified canopy and sky, into the 'edge detection' method. We showed that choosing a suitable neighbor distance for a photo with a specific gap fraction can significantly improve the accuracy of the original 'edge detection' method. We developed an ND-IS (Neighbor Distance-Iteration Selection) method that can automatically determine the threshold values of hemisphere photos with high accuracy and reproductivity. It combines the modified 'edge detection' method and an iterative selection method, with the aid of an empirical power function for the relationship between neighbor distance and manually verified gap fraction. This procedure worked well throughout a broad range of gap fractions (0.019–0.945) with different canopy compositions and structures, in five forest biomes along a broad gradient of latitude and longitude across Eastern China. Our results highlight the necessity of integrating neighbor distance into the original 'edge detection' algorithm. The advantages and limitations of the method, and the application of the method in the field were also discussed. [ABSTRACT FROM AUTHOR]

In the early stage of architectural design, addressing the challenges posed by negative spaces in high-density urban environments is crucial for enhancing spatial efficiency and building sustainability. Multiple studies employed digital methods and tools to address these issues, such as parametric design, simulation, and genetic algorithms, to investigate architectural generation approaches for urban negative spaces. This article proposes an integrated design process that involves finding the location and form of negative spaces, generating solutions using slime mold and wasp algorithms, and optimizing and analyzing solutions using the Wallacei plugin in Grasshopper. This comprehensive approach underscores the potential of parametric design to yield a multitude of solutions while also acknowledging the convergence challenges encountered during simulations, particularly in optimizing for optimal sunlight exposure during the winter solstice and minimal solar radiation in the summer. Analyzing the optimization goals and parameter values of the 15th Pareto optimal solution in the 100th generation reveals: (1) a higher number of units leads to positive correlation growth in both objectives; (2) within a certain number of units, parametrically generated solutions facilitate the convergence of optimization goals, yielding optimal outcomes. Therefore, factors such as the range of unit quantities and proportions need consideration during early-stage parametric design and simulation. This study explores a design methodology for negative spaces in high-density urban cities, validating the feasibility of various mainstream generation methods and offering insights for future research. [ABSTRACT FROM AUTHOR]

An extreme high-temperature event occurred in North China on 22 June 2023, with the maximum temperature reaching 41.8 °C. The high-temperature centers preferentially occurred at the foothills of the Taihang and Yanshan Mountains, indicating an important role of the underlying orography. In the present work, we study the orographic effects of this extreme high-temperature event according to high-resolution numerical simulations using the Weather Research and Forecasting model. The results show that the presence of the mountains in North China contributed notably to the high-temperature event, which can enhance the 2 m air temperature by up to 3 °C. In the daytime, the enhancement of temperature is primarily due to the diabatic heating of sensible heat flux at the terrain surface caused by solar shortwave radiation, whereas the well-known foehn effect has little contribution. Indeed, the dynamically forced downslope flow of foehn is totally suppressed by the upslope flow of the thermally driven mountain-plain circulation. In the nighttime, the sensible heat flux at the terrain surface changes to weakly negative, given the cooling of land surface longwave radiation. As a result, the enhancement of near-surface temperature at the terrain foothill is dominated by the adiabatic warming of downslope flow. Yet, the near-surface temperature far away from the mountain is enhanced by the subsidence warming of a synoptic anomalous anti-cyclone, which is induced by the diabatic heating over the mountains in the daytime. These findings help improve the understanding of the thermal and dynamical effects of orography on the occurrence of high-temperature events. [ABSTRACT FROM AUTHOR]

Vegetation plays a crucial role in maintaining the balance between nature, water and soil resources. However, understanding its impact mechanisms in arid and semi-arid areas remains limited. This study aims to analyze the spatial–temporal characteristics of the vegetation leaf area index (LAI) and climate elements in typical regions of northwest China and the correlations between LAI and climate elements; it also aims to explore the influence of regional vegetation growth on climate change. The results reveal significant correlations between LAI and various climate elements. Specifically, within the same region, surface temperature, precipitation, vegetation transpiration, and total evaporation show positive correlations with the LAI, whereas surface albedo shows a negative correlation. Vegetation may affect climate through both heat and water exchange between the land and atmosphere. Increased vegetation leads to the enhanced absorption of solar radiation by the land surface, elevating surface temperature. Increased levels of vegetation also increase vegetation transpiration and total evaporation, increasing the water vapor content in the atmosphere and thus leading to increased surface precipitation. Therefore, vegetation distribution plays a role in climate change, and ecological restoration projects in the northwest region hold significant potential for addressing ecological challenges in its arid and semi-arid areas. [ABSTRACT FROM AUTHOR]

This study aimed to utilize hybrid physics-based models that allow for a comprehensive evaluation of how solar–meteorological variables influence the fraction of absorbed photosynthetically active radiation (FAPAR). By integrating both physical and statistical approaches, these models provided a novel perspective on understanding the complex relationship between solar radiation and FAPAR dynamics. The integration of URE and CLC in studying ecosystem efficiency in the nations of China and India is crucial, owing to their interdependent nature. To achieve this, the coupled model intercomparison project phase 6 (CMIP6) was utilized, which involved interpolating data from the most recent five GCMs from the six-phase CMIP6. By integrating advanced modeling techniques with observational data, this innovative approach enabled a holistic assessment of the intricate connections between urban development, climate dynamics, and the productivity of surrounding ecosystems in these regions. The reported decline in FAPAR productivity in China and India highlights the growing concern over the impact of climate change on the health and resilience of vegetation. China experienced negative changes of −1.425, −0.414, and −0.018, while India reported negative changes of −2.463, 2.199, and 2.915 under different scenarios. Interestingly, despite these variations, both countries observed similar tidal variations in their seasonal bases, suggesting a consistent pattern in the fluctuations of vegetation productivity throughout the year. The authors of this study collected and analyzed extensive data on CLC and URE in order to investigate their impact on FAPAR fluctuations in China and India. The higher impact of URE compared to CLC in terms of effective contributions suggests that URE plays a crucial role in shaping the atmospheric physics of both China, accounting for 45.26%, and India, registering 33.95%. Conversely, the effects of CLC and PAR residual factor (PRF) were more pronounced in India compared to China, with impacts of 8.21% and 53.03% compared to 4.21% and 46.70%, respectively. [ABSTRACT FROM AUTHOR]

Insulated plastic greenhouses (IPG) were a new type of facility emerging in production in China. The mechanism of construct parameters on the indoor solar radiation deserves further study. A mathematical model that can well describe the spatial and temporal distribution of solar radiation in the greenhouse was established. Based on this model, the effects of multiple construct parameters, such as insulation blanket shading, height–span ratio, greenhouse azimuth, and geographical latitude, on the indoor solar radiation of IPG were quantitatively specified. The results showed that the spatial and temporal distribution of inside solar radiation was highly variable along the span direction of IPG. And by analyzing the simulation data, it was found that for different heights of greenhouses, the corresponding optimal span is different. Therefore, in the construction of greenhouses, the appropriate height–span ratio should be selected according to the planting demand. And by simulating the inside solar radiation under different greenhouse azimuth angles, it was found that accumulated daily solar radiation in the greenhouse reaches the optimum value when the greenhouse azimuth angle is in the range of 0–20°. This paper can provide theoretical guidance for the design and optimization of structure parameters of IPG in China. [ABSTRACT FROM AUTHOR]

This study introduces a Solar Energy-Powered Embedded Pipe Envelope System (SEPES) designed to enhance indoor thermal comfort and reduce heating loads during the heating season. To achieve this objective, a dynamic simulation model coupling a SEPES and building thermal environment was established under the TRNSYS environment. Based on the model, a case analysis was conducted to investigate the operational characteristics of the system during the heating season in a rural building in Beijing. The results indicate that, on the coldest heating day, the system can elevate the indoor temperature by 14.5 °C, reducing the daily heat load from 76.3 kWh to 20.3 kWh, achieving a remarkable energy savings of 73.4%. Additionally, due to the utilization of lower solar heat collection temperatures, the energy efficiency of the system reaches 26.9%. Throughout the entire heating season, the SEPES system enhances the natural indoor temperature by 13.3 °C to 16.6 °C, demonstrating significant effectiveness. Moreover, regional adaptability analysis indicates that the SEPES achieves energy savings ranging from 43.9% to 66% during the heating season in cold regions and regions with hot summers and cold winters in China. Overall, the SEPES is most suitable for climates characterized by both low temperatures and abundant solar radiation in order to achieve optimal performance. [ABSTRACT FROM AUTHOR]

Accurate estimation of desert vegetation transpiration is key to regulating desert water resources of desert ecosystems. Sap flow density (SFD) can indirectly reflect a tree's transpiration consumption, and it has been affected by climate warming and groundwater depths in desert ecosystems. Sap flow responses to meteorological conditions and groundwater depths are further affected by tree of different sizes. However, how meteorological factors and groundwater depths affects tree sap flow among tree sizes remains poorly understand. In this study, a 50 × 50 m P. euphratica stand was selected as a sample plot in the hinterland of the Taklamakan Desert, and the SFD of P. euphratica of different sizes was measured continuously using the thermal diffusion technique from May to October of 2021 and 2022. The results showed that SFD of large P. euphratica was consistently higher than that of small P. euphratica in 2021 and 2022. and the SFD of P. euphratica was significantly and positively correlated with solar radiation (Rad) and vapor pressure deficit (VPD), and the correlation was higher than that of the air temperature (Ta) and relative humidity (RH), and also showed a strong non-linear relationship. Analysis of the hour-by-hour relationship between P. euphratica SFD and VPD and Rad showed a strong hysteresis. Throughout the growing season, there was no significant relationship between SFD of P. euphratica and groundwater depth, VPD and Rad were still the main controlling factors of SFD in different groundwater depths. However, during the period of relative groundwater deficit, the effect of groundwater depth on the SFD of P. euphratica increased, and the small P. euphratica was more sensitive, indicating that the small P. euphratica was more susceptible to groundwater changes. This study emphasized that Rad and VPD were the main drivers of SFD during the growing season, as well as differences in the response of different sizes of P. euphratica to groundwater changes. The results of the study provide a scientific basis for future modeling of transpiration consumption in P. euphratica forests in desert oases, as well as the regulation and allocation of water resources. [ABSTRACT FROM AUTHOR]

Due to limited direct measurements, regional or global terrestrial evapotranspiration (ET) is generally derived from a combination of meteorological and satellite observations. Although the inhomogeneity of the observed climate data has been widely reported, its impact on the calculated ET has not been adequately quantified. This study aimed to calculate ET using the modified Penman‐Monteith (MPM) model with raw and homogenized meteorological data. Additionally, we compared the calculated ET with those estimates from variable methods (water balance, satellite‐based, and reanalysis) in China and its six major river basins from 1982 to 2020. During the overlapping period of 1997–2018, ET calculated from raw input data decreased slightly at −0.39 mm yr−2 (p = 0.64) in China, whereas homogenized ET showed a significant increasing trend of 0.93 mm yr−2 (p = 0.02), with a better agreement with water balance ET (1.93 mm yr−2, p = 0). Global Land Evaporation Amsterdam Model (GLEAM) and Modern‐Era Retrospective Analysis for Research and Applications, version 2 (MERRA2) could reproduce the increasing trends with 2.08 mm yr−2 (p = 0) and 2.59 mm yr−2 (p = 0). The intercomparison of input variables (solar radiation, relative humidity, wind speed, precipitation, and air temperature) among ET products revealed substantial differences, which can account for the discrepancies in ET estimates. Homogenized ET, GLEAM and MERRA2 exhibited significant increasing trends in China and most river basins from 1982 to 2020. Our findings underscore the importance of utilizing homogenized input data for more accurate ET estimation. Plain Language Summary: Regional or global terrestrial evapotranspiration (ET) is generally derived from a combination of meteorological and satellite observations. Previous studies have highlighted inhomogeneity issues in meteorological observations in China, which can introduce uncertainty to the ET estimated. However, the uncertainty of ET estimates has not been quantified. In this study, we recalculated ET in China using an energy partition model with raw and homogenized meteorological inputs from 1982 to 2020. We also compared ET estimates obtained from different methods (water balance, satellite‐based, and reanalysis) to analyze trends in China and its major river basins over the same period. The study revealed that inhomogeneity in the input data distorted the calculated trends of ET. Key Points: Calculated China's ET using raw and homogenized data in the modified Penman‐Monteith modelImproved ET trend with homogenized data, aligning better with water balance ET over ChinaHomogenized ET exhibits a significant increasing trend of 0.90 mm yr−2 (p = 0) from 1982 to 2020 across China [ABSTRACT FROM AUTHOR]

The National Standard of China has recommended the typical meteorological year (TMY) method for assessing solar energy resources. Compared with the widely adopted multi-year averaging (MYA) methods, the TMY method can consider the year-to-year variations of weather conditions and characterize solar radiation under climatological weather conditions. However, there are very few TMY-based solar energy assessments on the scale of China. On the national scale, the difference between the TMY and MYA methods, the requirement of the data record length, and the impacts of the selection of meteorological variables on the TMY-based assessment are still unclear. This study aims to fill these gaps by assessing mainland China's solar energy resources using the TMY method and China Meteorological Forcing Dataset. The results show that the data record length could significantly influence annual total solar radiation estimation when the record length is shorter than 30 years. Whereas, the estimation becomes stable when the length is greater or equal to 30 years, suggesting a thirty-year data record is preferred. The difference between the MYA and TMY methods is exhibited primarily in places with modest or low abundance of solar radiation. The difference is nearly independent of the examined data record lengths, hinting at the role of regional-specific weather characteristics. The TMY and MYA methods differ more pronounced when assessing the seasonal stability grade. A total of 7.4% of the area of China experiences a downgrade from the TMY relative to the MYA methods, while a 3.15% area experiences an upgrade. The selection of the meteorological variables has a notable impact on the TMY-based assessment. Among the three meteorological variables examined, wind speed has the most considerable impact on both the annual total and seasonal stability, dew point has the second most significant impact, and air temperature has the least. The results are useful for guiding future research on solar energy assessment in China and could be helpful for solar energy development planning. [ABSTRACT FROM AUTHOR]

Atmosphere clearness is the single most essential parameter determining surface solar radiation. However, few studies have investigated the interannual variations in China's atmosphere clearness and the impacts of El Niño–Southern Oscillation on it. This study aims to fill the knowledge gap by calculating the clearness index using the China Meteorological Forcing Dataset version 1.7 and then analyzing the correlations between the interannual anomaly and the Niño-3.4 index. The results show that there is a significantly negative correlation in the southeastern coastal regions, northern Xinjiang, northeastern Xizang, and areas from northern Hebei to middle Inner Mongolia. In these areas, the higher the ENSO index, the lower the clearness index, and, coincidingly, positive precipitation anomalies are reported in previous studies. The impacts of El Niño and La Niña vary with seasons. The ENSO events have generally opposite impacts in the seasons other than summer. El Niño tends to decrease the clearness index, while La Niña tends to decrease the atmosphere clearness in most territories of China. The impacts are different in the places of statistical significance. The negative impacts of El Niño are significant in the southeastern coastal regions of China in winter and in northeast China in autumn. The positive impacts of La Niña are significant in the southwestern and northeastern China in the autumn. In these seasons, ENSO's impacts scale with the strength of the event. Stronger events amplify the magnitude of the anomalies, while the spatial patterns of the anomalies are kept almost invariant. In summer, ENSO's impacts exhibit different characteristics than in the other seasons and between the El Niño and La Niña events. For the El Niño events, the impacts are insignificant in most territories of China, even for the strong ones. La Niña has a larger influence on the summertime clearness than El Niña, and the spatial pattern of the La Niña's impacts varies with the event strength. The anomalies during strong La Niña events have a tripolar pattern with a positive anomaly in south and north China and a negative anomaly in between. The pattern suggests that the relationship between the clearness index and precipitation is different during strong La Niña events. These findings would provide valuable insights into the interannual variations of atmosphere clearness in China and could be clues to further investigation. The revealed impacts of El Niño–Southern Oscillation should be important for developing solar energy. [ABSTRACT FROM AUTHOR]

BACKGROUND: The Huang–Huai–Hai Plain (3HP) is the main agricultural area in China. Although climate change (CC) and crop management (CM) are considered factors affecting the winter wheat net primary production (NPP) in this region, their effects remain unclear. In the present study, we evaluated the relative contributions of CC and CM to winter wheat aboveground NPP (ANPP) in the 3HP and the relationships between climatic factors and ANPP using the first‐order difference method from 2000 to 2020. RESULTS: CM had a greater influence on the ANPP of winter wheat than did CC. However, the relative contribution of CM to ANPP gradually decreased in humid and dry sub‐humid regions with the development of winter wheat. Furthermore, in areas characterized by low temperatures and limited precipitation, CC became the dominant factor contributing to ANPP, indicating that varieties resilient to drought and cold should be selected in these regions. Minimum and average temperatures were the dominant factors driving spatiotemporal variations in ANPP during the early stage of winter wheat growth, whereas maximum temperature constrained growth throughout the winter wheat growth cycle. When winter wheat entered the vigorous growth stage, precipitation and solar radiation replaced temperature as the driving factors influencing winter wheat growth. CONCLUSION: The results of the present study provide guidance for optimizing winter wheat crop management in the 3HP. © 2023 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

The New Baseline Surface Radiation (NBSR) system was established at the Shangdianzi (SDZ) regional Global Atmosphere Watch (GAW) station in 2013 to observe nine broadband radiation components, i.e. the global, direct, diffuse, and upwelling shortwave irradiance (GSWI, DSWI, DifSWI, and UpSWI); the photosynthetically active radiation (PAR); the ultraviolet irradiance (UVAI and UVBI); and the down- and upwelling longwave irradiance (DnLWI and UpLWI). To test the 1 min raw radiometric data, a Hybrid Algorithm for Radiation Data Quality Control (HARDQC) is presented in this study based on well-established methods, together with the solar irradiance dataset and the spectral features of the instrument bands. Subsequently, a NBSR dataset, which consists of radiation data at multiple timescales (i.e. 1 min, hourly, daily, monthly, monthly average hourly, and monthly average daily) over 2013–2022, is established and evaluated. Results show that more than 98.7 % of all radiation components passed the physical possibility test. The percentages of those that passed the extremely rare test are greater than 98.6 % for all radiation components except for the DnLWI (97.1 %). The percentages of those that passed the comparison test are greater than 83.3 % (GSWI), 78.3 % (DSWI), 81.7 % (DifSWI), 93.1 % (UpSWI), 88.9 % (PAR), 95.6 % (UVAI), 96.3 % (UVBI), 99.8 % (DnLWI), and 99.7 % (UpLWI), respectively. Due to data logger faults, removal of the instruments for calibration, and lightning strikes, some apparent data gaps in the upwelling radiation components (January 2015–August 2017) and all radiation components (December 2018; July to September 2021) were detected. Despite the existence of a few imperfections in the NBSR dataset, it is still reliable to apply it in many fields such as the validation of satellite products and numerical models, the investigation of relationships between radiation and atmospheric composition, and the detection of changes in the surface fluxes. The dataset described in this paper is available at 10.1594/PANGAEA.963330 (Quan et al., 2023b). [ABSTRACT FROM AUTHOR]

Accurately estimated reference evapotranspiration (ET0) is essential to regional water management. The FAO recommends coupling the Penman–Monteith (P‐M) model with the Ångström–Prescott (A‐P) formula as the standard method for ET0 estimation with missing Rs measurements. However, its application is usually restricted by the two fundamental coefficients (a and b) of the A‐P formula. This paper proposes a new method for estimating ET0 with missing Rs by combining machine learning with physical‐based P‐M models (PM‐ET0). The benchmark values of the A‐P coefficients were first determined at the daily, monthly, and yearly scales, and further evaluated in Rs and ET0 estimates at 80 national Rs measuring stations. Then, three empirical models and four machine‐learning methods were evaluated in estimating the A‐P coefficients. Machine learning methods were also used to estimate ET0 (ML‐ET0) to compare with the PM‐ET0. Finally, the optimal estimation method was used to estimate the A‐P coefficients for the 839 regular weather stations for ET0 estimation without Rs measurement for China. The results demonstrated a descending trend for coefficient a from northwest to southeast China, with larger values in cold seasons. However, coefficient b showed the opposite distribution as the coefficient a. The FAO has recommended a larger a but a smaller b for southeast China, which produced the region's largest Rs and ET0 estimation errors. Additionally, the A‐P coefficients calibrated at the daily scale obtained the best estimation accuracy for both Rs and ET0, and slightly outperformed the monthly and yearly coefficients without significant difference in most cases. The machine learning methods outperformed the empirical methods for estimating the A‐P coefficients, especially for the sites with extreme values. Further, ML‐ET0 outperformed the PM‐ET0 with yearly A‐P coefficients but underperformed those with daily and monthly ones. This study indicates an exciting potential for combining machine learning with physical models for estimating ET0. However, we found that using the A‐P coefficients with finer time scales is unnecessary to deal with the missing Rs measurements. [ABSTRACT FROM AUTHOR]

Due to a robust emission control policy, significant reductions in major air pollutants, such as PM 2.5 , SO 2 , NO 2 , and CO, were observed in China between 2015 and 2020. On the other hand, during the same period, there was a notable increase in ozone (O 3) concentrations, making it a prominent air pollutant in eastern China. The annual mean concentration of maximum daily 8 h average (MDA8) O 3 exhibited alarming linear increases of 2.4, 1.1, and 2.0 ppb yr -1 (ppb is for parts per billion) in three megacity clusters: Beijing–Tianjin–Hebei (BTH), the Yangtze River Delta (YRD), and the Pearl River Delta (PRD), respectively. Meanwhile, there was a significant 3-fold increase in the number of O 3 -exceeding days, defined as MDA8 O 3 > 75 ppb. Our analysis indicated that the upward increases in the annual mean concentration of MDA8 were primarily driven by the rise in consecutive O 3 -exceeding days. There were expansions of high O 3 in urban centers to rural areas accompanied by a saturation effect so that MDA8 O 3 concentrations at the high-O 3 stations in 2015 remained nearly constant at 100 ppb. Last, we found a close association between O 3 episodes with 4 or more consecutive O 3 -exceeding days and the position and strength of tropical cyclones (TCs) in the northwest Pacific and the West Pacific subtropical high (WPSH). The TC and WPSH contributed to meteorological conditions characterized by clear skies, subsiding air motion, high vertical stability in the lower troposphere, increased solar radiation, and a positive temperature anomaly at the surface. These favorable meteorological conditions greatly facilitated the formation of O 3. Thus, we propose that the worsening O 3 increases observed in the BTH, YRD, and PRD regions from 2015 to 2020 can be mostly attributed to enhanced photochemical O 3 production resulting from an increased occurrence of meteorological conditions with high solar radiation and positive temperature anomalies under the influence of the WPSH and TCs. [ABSTRACT FROM AUTHOR]

Existing land surface models still have large error in the simulation of surface albedo. At present, introducing the influence of meteorological factors into the albedo parameterization scheme is an effective way to improve the albedo simulation. However, the improvement of the vegetation canopy surface albedo parameterization scheme is still insufficient. Therefore, based on the radiation and meteorological observation data during 1 November 2015–30 April 2018 from a land‐atmosphere interaction observation tower in a typical secondary evergreen broadleaved forest in Southern China, this study firstly analyzed the influencing factors of canopy surface albedo. It was found that the solar elevation angle and air relative humidity are two key factors influencing the canopy surface albedo, and the impact of the solar elevation angle on canopy surface albedo is not exactly the same on diurnal and seasonal scales. Then, two new canopy surface albedo parameterization schemes of additive form and multiplicative form were proposed and introduced into CLM5 model for single point simulation. Results show that the adoption of newly developed canopy surface albedo parameterization schemes can improve the simulation of diurnal and seasonal variations of albedo and reduce the overestimation of the simulation of near‐infrared radiation albedo and visible radiation albedo in the CLM5 model, then significantly reduce the root mean square errors of reflected solar radiation and net radiation simulation. Plain Language Summary: Surface albedo is an important factor affecting the heat exchange between land and atmosphere, and is critical for weather forecasting and regional climate modeling. At present, even in some complex land surface models, the canopy surface albedo parameterization scheme only considers the variation of leaf and stem index and the cosine of the solar zenith angle over time but does not take into account the effect of meteorological factors. In this study, two newly developed canopy albedo schemes that take into account the effects of solar elevation angle and air relative humidity were developed, and then introduced into the CLM5 model for single point simulation. The results show that the new canopy albedo scheme can indeed improve the simulation skills of the reflected solar radiation. The results emphasize the important influence of meteorological factors on the surface albedo, and the findings may provide a base to further develop and improve the CLM5 model. Key Points: Two new canopy surface albedo schemes considering the influence of solar elevation angle and air relative humidity are proposedThe new canopy surface albedo schemes can reduce the canopy surface albedo of evergreen broad‐leaved forest overestimated by CLM5 model [ABSTRACT FROM AUTHOR]

High concentrations of ground-level ozone (O3) pose a significant threat to human health. Obtaining high-spatiotemporal-resolution information about ground-level O3 is of paramount importance for O3 pollution control. However, the current monitoring methods have a lot of limitations. Ground-based monitoring falls short in providing extensive coverage, and remote sensing based on satellites is constrained by specific spectral bands, lacking sensitivity to ground-level O3. To address this issue, we combined brightness temperature data from the Himawari-8 satellite with meteorological data and ground-based station data to train four machine learning models to obtain high-spatiotemporal-resolution information about ground-level O3, including Categorical Boosting (CatBoost), eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LGBM), and Random Forest (RF). Among these, the CatBoost model exhibited superior performance, achieving a ten-fold cross-validation R2 of 0.8534, an RMSE of 17.735 μg/m3, and an MAE of 12.6594 μg/m3. Furthermore, all the selected feature variables in our study positively influenced the model. Subsequently, we employed the CatBoost model to estimate averaged hourly ground-level O3 concentrations at a 2 km resolution. The estimation results indicate a close relationship between ground-level O3 concentrations and human activities and solar radiation. [ABSTRACT FROM AUTHOR]

The changes of annual average, minimum and maximum temperature (Tavg, Tmin and Tmax) in China over 1961–2020 were examined based on the Chinese gridded observation dataset (CN05.1), and the relative contributions of Tmin and Tmax to the tendency of Tavg were addressed. The results show that the nationwide mean warming rate reached 0.29°C·decade−1 for Tavg, 0.39°C·decade−1 for Tmin and 0.24°C·decade−1 for Tmax, respectively, from 1961 to 2020, which depended on temporal windows. The relative contributions of Tmin and Tmax to the trends of Tavg over China during 1961–2020 are 56% and 44%, respectively. On the spatial scale, the majority of China exhibits a greater relative contribution of Tmin than Tmax, except for some areas along the Hu Huanyong Line. As in the case of the warming rate, the temporal patterns of the magnitude of the relative contribution of Tmin and Tmax are also identified. For the first 30 years (1961–1990), Tmin is the primary contributor to changes in the trend of Tavg over a broad area excluding the Qinling Mountain‐Huai River to the Nanling Mountain. The national average magnitudes of the relative contribution of Tmax is slightly larger than Tmin in the latter 30 years (1991–2020). The areas with larger contributions of Tmax are mainly scattered along Hu Huanyong Line, the southern part of the Qinghai‐Tibet Plateau and the patchy parts of Northwest China. Changes in downward longwave radiation, solar radiation, cloud cover, land use, soil moisture, atmospheric circulation and urbanization likely accounted for much of the trend difference of Tmin and Tmax, thereby leading to various relative contributions across different regions during different periods. The warming mechanisms in China have been subjected to novel changes. The study could provide information for decision making on scientific response to warming. [ABSTRACT FROM AUTHOR]

During the Cretaceous, there were two factors that had important influences on the East Asian climate, the East Asian coastal mountains and Earth's orbital cycling. An important question is how the coastal mountains modulated the variability of East Asian climate over orbital timescales. Here, we perform simulations with the coastal mountains of 0, 2, and 4 km high and three orbital configurations to answer the question. Our results show that a mountain range at the East Asian coast can amplify the impacts of orbital forcing on East Asian climate. Specifically, precipitation over the Songliao Basin in Northeastern China has significant changes as the coastal mountain range is about 4 km high. Combining our simulation results with orbitally‐controlled sedimentary deposits from the Songliao Basin, we conclude that the altitude of the coastal mountain range was very likely higher than 2 km in the Late Cretaceous. Plain Language Summary: Tectonic events and solar insolation are the two important factors impacting variations of the climate system in the geological past. Regional climate responses to variations in the radiation from the sun over 104–105 years were often magnified or dampened by tectonic events. Cretaceous sedimentary records in East Asia suggest that East Asian climate was influenced by the solar insolation. Geological evidence showed that a mountain range existed along the East Asian coast then. Would this mountain range modulate impacts of solar insolation on East Asian climate? Our modeling results show that the influence of solar insolation on East Asian climate can be amplified by the coastal mountain range, depending on the mountain elevation. When the coastal mountain range is ∼2 km high, the amplification effects become significant. When its altitude reaches ∼4 km, the response of East Asian climate to solar insolation is considerably strengthened, and such a condition is supported by the rhythm induced by the climate variation due to solar insolation archived in the Cretaceous strata in the Songliao Basin. Thus, we speculate that the East Asian coastal mountains might have reached an altitude more than 2 km in the Late Cretaceous. Key Points: East Asian climate was sensitive to orbital forcing in the Late CretaceousEast Asian coastal mountains amplified orbital forcing on East Asian climate variabilityEast Asian coastal mountains were likely higher than 2 km in the Late Cretaceous [ABSTRACT FROM AUTHOR]

The interpretation of stalagmite δ18O in terms of reflecting Asian summer monsoon (ASM) precipitation is still elusive. Here, we present high‐resolution stalagmite trace element ratios (X/Ca, X = Mg, Sr, Ba) records from southwest China covering 116.09 to 4.07 ka BP. δ18O, δ13C, and X/Ca values exhibit clear precessional cycles, with δ18O values reflecting ASM circulation/intensity, while X/Ca ratios capture local precipitation or evapotranspiration variations. Our results show that Northern Hemisphere summer insolation (NHSI) is the main driver of ASM intensity and precipitation phase variation, but global ice volume modulates the response magnitude of summer precipitation to insolation. During the Last Glacial Maximum, high ice volumes caused significant monsoon precipitation to decrease. In contrast to modern observations of the tripolar distribution of precipitation in China, our record is consistent with paleo‐precipitation records in southern and northern China. Plain Language Summary: While it is well known that global changes have led to variations in the intensity and spatial distribution of Asian monsoon precipitation, the mechanisms behind this are not well understood. Paleoclimate records are essential for revealing the drivers behind monsoon variation. However, speleothem records from the Asian monsoon region rarely provide direct information on the amount of rainfall. Here we report on multiple indicator data sets from a stalagmite in southwestern China. It could help explore the variation of monsoon precipitation over the last ∼100,000 years. We find that the increase/decrease of Northern Hemisphere summer insolation controls the increase/decrease of Asian summer monsoon rainfall. In addition, global ice volume moderates the magnitude of rainfall response to insolation, and precipitation decreases significantly during high ice volume periods. Based on the present paleo‐precipitation records evidence, the existence of the spatial pattern of increasing/decreasing rainfall in central China corresponding to decreasing/increasing rainfall in northern and southern China remains ambiguous on the orbital scales, although the feature has been captured by some of the model simulations. Key Points: Stalagmite trace elements are indicators of regional hydrological environmental variations in Southwestern ChinaNorthern Hemisphere summer insolation and global ice volume modulate the phase and amplitude variations of regional hydrological environmentThe meridional tripolar spatial pattern of precipitation in monsoon region in China on the orbital scale remains ambiguous [ABSTRACT FROM AUTHOR]

With the intensification of global change, the overall functions and structures of ecosystems in sub-humid to arid transition regions have changed to varying degrees. The Loess Plateau, as a typical case of such regions, plays a great role in the study of net primary productivity (NPP) for estimating the sustainability of the Earth's carrying capacity in terrestrial ecosystem process monitoring. In the research on contrasting response mechanisms and ecological stress of NPP, the main innovations of this study are as follows. On the basis of the indicator system constructed from satellite imagery and meteorological data, we introduced deep multiple regressive models to reveal the relationship between NPP and the identified driving factors and then creatively proposed ecological stress (ES) evaluation models from the perspective of vegetation productivity. The findings are as follows: 1) From 2000 to 2019, the changes in driving factors presented a clear regional character, and the annual NPP maintained a fluctuating increasing trend (with a value of 4.57 g-m2-a-1). From the perspective of spatial distribution, the growth rate of NPP gradually increased from arid to sub-humid regions. 2) The effects of different driving factors on NPP changes and specific NPPs varied greatly across different regions. Arid and semi-arid regions were mainly controlled by precipitation (20.49%), temperature (15.21%), and other related factors, whereas sub-humid regions were mainly controlled by solar radiation, such as net surface solar radiation (NSSR) (8.71%) and surface effective radiation (SER) (7.93%). The main driving factors of NPP change varied under different soil conditions. 3) The spatio-temporal patterns of NPP approximated those of ES, but the effects of the latter significantly differed across ecological functional regions and land uses. This research on the Loess Plateau can serve as a valuable reference for future research on realizing ecosystem restoration and protection in sub-humid to arid transition regions. [ABSTRACT FROM AUTHOR]

The Ångström–Prescott formula is commonly used in climatological calculation methods of solar radiation simulation. Aiming at the characteristics of a vast area, few meteorological stations, and uneven distribution in the tropical regions of China, in order to obtain the optimal parameters of the global solar radiation calculation model, this study proposes a suitable monthly global solar radiation model based on the single-station approach and the between-groups linkage of the A–P model, which utilizes monthly measured meteorological data from 80 meteorological stations spanning the period from 1996 to 2016 in the tropical zone of China, considering the similarity in changes of monthly sunshine percentage between stations. The applicability and accuracy of the correction parameters (a and b coefficients) were tested and evaluated, and then the modified parameters were extended to conventional meteorological stations through Thiessen polygons. Finally, the spatial distribution of solar radiation in the tropical region of China was simulated by kriging, IDW, and spline interpolation techniques. The results show the following: (1) The single-station model exhibited the highest accuracy in simulating the average annual global solar radiation, followed by the model based on the between-groups linkage. After optimizing the a and b coefficients, the simulation accuracy of the average annual global solar radiation increased by 5.3%, 8.1%, and 4.4% for the whole year, dry season, and wet season, respectively. (2) Through cross-validation, the most suitable spatial interpolation methods for the whole year, dry season, and wet season in the tropical zone of China were IDW, Kriging, and Spline, respectively. This research has positive implications for improving the accuracy of solar radiation prediction and guiding regional agricultural production. [ABSTRACT FROM AUTHOR]

• Stratification strategy based on the decision tree model for attributing vegetation change drivers. • Vegetation photosynthesis exhibited an improvement trend in 81.5% of areas in China. • Changes in vegetation photosynthesis were mainly caused by climate change. Vegetation photosynthesis plays a key role in maintaining carbon balance in terrestrial ecosystems. Solar-Induced chlorophyll Fluorescence (SIF) was utilized as a proxy for vegetation photosynthesis in this study, and the Theil-Sen and Mann-Kendall methods were used to examine trends of vegetation change in China. The impacts of climate conditions on vegetation photosynthesis were examined with the correlation analysis and recursive feature elimination (RFE) method. To distinguish the contributions of climate change and human activities on vegetation photosynthesis, land use data and SIF change trends were employed as inputs to a decision tree model. The results revealed that 81.5% of the areas in China exhibited an improving trend of vegetation photosynthesis. Vegetation degradation areas were mainly located in Northern arid and semi-arid region (NASR), Qinghai Tibet Plateau (QTP) and Huang-Huai-Hai Plain (HHHP). Precipitation dominated the effect of vegetation photosynthesis in these regions. The increase of vegetation photosynthesis in the southern regions was positively impacted by solar radiation. The growth of agricultural technology obviously benefited the improvement of vegetation photosynthesis in the Middle-lower Yangtze Plain (MLYP) and Northeast China Plain (NCP). Furthermore, the establishment of ecological conservation programs has helped to restore vegetation in arid and semi-arid areas. The detrimental effects of human activities included overgrazing disturbance and coal mining activities, which resulted in vegetation deterioration in areas of QTP and HHHP. The methodologies and findings of this study can help to comprehend changes in vegetation dynamics and give a theoretical foundation for evaluating regional ecological conservation initiatives. [ABSTRACT FROM AUTHOR]

The frequent occurrence of dry and hot (DH) days in South China in summer has a negative impact on social development and human health. This study explored the variation characteristics of DH days and the possible reasons for this knotty problem. The findings revealed a notable increase in the number of DH days across most stations, indicating a significant upward trend. Additionally, DH events were observed to occur frequently. The number of DH days increased during 1970–1990, decreased from 1991 to 1997, and stayed stable after 1997. The key climate factors affecting the interannual variability of the number of DH days were the Indian Ocean Basin warming (IOBW) in spring and the East Asian Summer Monsoon (EASM). Compared with the negative phase of IOBW, in the positive phase of IOBW, 500 hPa and 850 hPa geopotential height enhanced, the West Pacific subtropical high strengthened and extended abnormally to the west, more solar radiation reached the surface, surface outgoing longwave radiation increased, and there was an anomalous anticyclone in eastern South China. The atmospheric circulation characteristics of the positive and negative phases of ESAM were opposite to those of IOBW, and the abnormal circulation of the positive (negative) phases of ESAM was unfavorable (favorable) for the increase in the number of DH days. A long-term prediction model for the number of summer DH days was established using multiple linear regression, incorporating the key climate factors. The correlation coefficient between the observed and predicted number of DH days was 0.65, and the root-mean-square error was 2.8. In addition, independent forecasts for 2019 showed a deviation of just 1 day. The results of the independent recovery test confirmed the stability of the model, providing evidence that climatic factors did have an impact on DH days in South China. [ABSTRACT FROM AUTHOR]

Accurately forecasting global solar radiation plays a key role in photovoltaic evaluations. To quantify and control the uncertainties in global solar radiation forecasting, this study developed a robust and accurate forecasting model. This was constructed in the reproducing kernel Hilbert space with a novel regularization. Global solar radiation datasets were collected from the autonomous region of Tibet in China. Experimental results demonstrate that the proposed model can quantify uncertainties and obtain more accurate forecasting compared with machine learning models. [ABSTRACT FROM AUTHOR]

The paper is based on the monthly price data of major livestock products in China from January 2002 to July 2022, the UVSVM model was used to investigate the dynamic feedback relationship between random price fluctuations and prices of three types of livestock products: Pork, beef and mutton. The result indicates that the random fluctuations in pork prices have a significant impact on pork prices and change in pork price levels will have a positive impact on the short-term random fluctuation in pork price. The random fluctuations in beef and mutton prices have little impact on their respective price. Changes in the price level of beef and mutton will have a negative impact on the random fluctuations in short-term prices. It is proposed to improve the regulatory mechanism of pork reserves and achieve stable supply and prices in the pork market, policy recommendations for taking multiple measures to stabilize the supply of beef and mutton and promote the stable operation of beef and mutton prices, in order to ensure the supply of livestock product and promote high-quality development of agriculture and rural economy. [ABSTRACT FROM AUTHOR]

Cotton (Gossypium hirsutum L.) is an economically important crop in China, and responses of cotton yield in different regions to separate and joint changes in natural and anthropogenic factors are the foundation for sustainable development under climate change; however, these remain uncertain. Here, we analyzed the spatiotemporal evolution and heterogeneity of cotton cultivation in China from 1949 to 2020 and quantified the response of cotton yield variations in air temperature, precipitation, solar radiation, disaster, and crop management factors between 1980 and 2020 by the Pettitt mutation test and GeoDetector. Multi-site meteorological data were obtained from different cotton-growing regions and corresponding cotton yield and phenology data were obtained from provinces. Our findings showed that all 17 Chinese provinces experienced advancements in cotton yield. Relative to 1949–1967, China's cotton production in 2007–2020 increased by 400% while cotton yield increased by 420%. Increases in factors such as minimum temperature (TES), average temperature (ADT), effective accumulated temperature (EAT), precipitation (PP), daily solar radiation (SSD), non-farm employment opportunities (O), disaster area (D), geographic region (GEO) and agricultural technologies like fertilizer usage (F), genetically modified varieties (Bt), and mechanized farming (M) have contributed to the enhanced cotton yield. The importance of single factors influencing cotton yield of China in descending order was as follows: F > Bt > M > GEO > EAT > O > PP > TES > ADT > SSD > D. However, the effects of different climatic and agriculture technological elements on cotton yield are spatially heterogeneous by region, and the combined effects of those elements are higher than those of single elements. The effects of driving factors vary across regional scales. The most significant interaction effects were observed between chemical fertilizer use and other driving factors. Specifically, the interaction between F and TES has the greatest explanatory influence in Northwest China. Our findings provide a reference for the development of more accurate adaptation strategies and management measures in different regions. We recommend that policymakers prioritize measures such as improving climate-resilient cotton varieties, encouraging technological advancements, and implementing policies that support equitable distribution of cultivation. [ABSTRACT FROM AUTHOR]

Numerical weather prediction (NWP) can provide vital information for pan evaporation (Ep) forecasts for the 16 days ahead, which is of great help to water resources management. However, the information for forecasting Ep usually requires bias corrections. This study was based on three bias correction methods [the equidistant cumulative distribution function method (EDCDFm; M1), XGBoost (XGB) with a single meteorological factor input (M2), and XGB with multiple meteorological factor input (M3)] and the meteorological data from 18 weather stations in southern China, the bias correction of meteorological factors forecasted by the second-generation Global Ensemble Forecast System (GEFSv2) was carried out. The results indicated the bias correction ability of the M3 method for GEFSv2 outputs was better than that of the M1 and M2 methods. It was a model-data error between GEFSv2 outputs and the corresponding observation data. Solar radiation exhibited the lowest error, whereas minimum temperature exhibited the highest. However, the M3 method decreased the forecast model-data error. In addition, this study compared the ability of three tree-based models to forecast Ep , namely, M5Tree (M5T), random forest (RF), and XGB. The XGB model had the highest forecasting accuracy for Ep. When the NWP outputs corrected by M1, M2, and M3 methods were used as the input of the XGB model, the averages of mean absolute errors (MAEs) at the 18 stations during the 1–16 day period ranged at 0.99–1.69, 0.78–1.14, and 0.78–1.07 mm/day , respectively. EP forecast showed the most significant error in the summer. Further, the relative humidity contributed the most to the Ep forecasting error. By addressing the issue of NWP outputs applied to Ep forecast, this study improves understanding of the bias correction method of NWP outputs and tree-based models to forecast Ep. It also improves understanding of the seasonal performance of Ep forecast and the impact of meteorological factors on forecast error that can inform future studies and models. [ABSTRACT FROM AUTHOR]

The offshore region of East Asia has a crucial role in recycling precipitation, especially in the current context of a warming climate. This is because the atmospheric feedback from the sea offshore East Asia directly impacts and modifies precipitation patterns by influencing the seasonal cycles of the surface energy and water budgets. We used a regional climate model incorporating sea–air coupling to investigate and better understand these climate feedback mechanisms in East Asia. We identified a reduction in precipitation caused by sea-air coupling over East Asia during the time period 1991 − 2014 under present day conditions. Specifically, we observed an average decrease in precipitation of about 0.1 ± 0.40 mm day−1 during June–July–August. This decrease in precipitation can be attributed to a combination of factors, including the effects of upward solar radiation, the asymmetry of the thermal contrast between the land and the sea, decreased evaporation in the southeastern ocean and the weakened transport of water vapor from the sea to the land. Our research suggests that the decrease triggered by sea–air coupling will be partially alleviated under future conditions, although not completely reversed, in terms of its impact on precipitation in eastern China. Although some relief is anticipated, the overall influence of sea–air coupling on patterns of precipitation in East Asia will persist, especially south of the Yellow River in eastern China. [ABSTRACT FROM AUTHOR]