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The reduction of the runoff to lake has become a common phenomenon of the inland lakes in northwest China, and the quantitative calculation of the driving factors affecting the runoff change has always been a challenge in lake research. In this pursuit, the present study was performed on Lake Daihai as an example, based on the relevant data from 1960 to 2018. The quantitative calculations of the runoff driving factors of Lake Daihai from 1982 to 2004 and from 2004 to 2017 were based on the slope change ratio of cumulative quantity (SCRCQ). It was concluded that human activities were the main factors affecting runoff change after 1982. The SCRCQ method was improved, which could carry out the quantitative calculations on the driving factors affecting the runoff change of the Lake Daihai from 1961 to 2017 on a yearly basis. The results show that the dominant factor of runoff changes in Lake Daihai was found to be the climate change before 1975, and it changed into human activities after 1975. Finally, based on the comparative analysis of the results of SCRCQ and improved SCRCQ, the advantages of improved SCRCQ were discussed. In addition, the reasons for the variation of the runoff to lake dominated by different factors in different periods for Lake Daihai were analyzed. Key Points: This paper analyzes the influence of climate change and human activities on the change in runoff volume of Lake Daihai according to the slope change ratio of cumulative quantity (SCRCQ)The SCRCQ method was improved, which could carry out the quantitative calculations on the driving factors affecting the runoff change of the Lake Daihai from 1961 to 2017 on a yearly basisThe reasons for the variation of the runoff to lake dominated by different factors in different periods for Lake Daihai were analyzed [ABSTRACT FROM AUTHOR]

This study analyzes the change characteristics of compound extreme events (CEEs) of temperature and precipitation (including warm‐wet, warm‐dry, cold‐wet and cold‐dry) in China on interannual and interdecadal scales between 1901 and 2019. The results demonstrate a long‐term increasing trend and interdecadal oscillations in CEEs total frequency. However, the frequency of each type of CEEs changes in a different manner compared with total CEEs frequency. There are fewer CEEs but increasing warm‐dry during 1901–1950. The period 1951–1995 are characterized by frequent cold CEEs (cold‐wet and cold‐dry), cold‐wet are largely distributed in most areas except for northeast and coastal areas of China, while cold‐dry are distributed in most areas except for the northwest regions of China. There are frequent warm CEEs (warm‐wet and warm‐dry) and fewer cold CEEs during 1996–2019. Warm‐wet frequently occurs in the Tibetan Plateau and northwest China, and warm‐dry mainly concentrates in southwest and northern China during this period. The frequency of warm‐dry and cold‐wet were higher than that of warm‐wet and cold‐dry over the past 119 years, whereas warm‐wet increased fastest in the northwest region after 1996, consistent with the warming and wetting characteristics in the northwest region of China. Further study show that long‐term change and low frequency oscillations have the greatest impact on CEEs among different time scale factors. Furthermore, the temperature rise caused by climate change affects the interdecadal characteristics of CEEs in China through the changes of circulation fields such as East Asian trough and subtropical high and the configuration between them. Plain Language Summary: An isolated analysis of univariate extreme events may seriously underestimate the losses caused by multivariable compound extreme events (CEEs). Evolution characteristics of CEEs of temperature and precipitation in China during the past 119 years were investigated. The CEEs in China over the past 119 years experienced three stages: 1901–1950 with less CEEs, 1951–1995 with frequent cold CEEs and 1996–2019 with frequent warm CEEs. Total frequency of CEEs showed an increasing trend but each type of CEEs had an obvious interdecadal variation. The frequency of warm‐dry and cold‐wet was higher than that of warm‐wet and cold‐dry, whereas warm‐wet increased fastest among the four CEEs after 1996. Long‐term change and low frequency oscillations have the greatest impact on CEEs among long‐term change and low frequency oscillations, changes in the variability of higher frequencies and coupling changes between temperature and precipitation. Key Points: Total frequency of compound extreme events (CEEs) shows long‐term increasing trends and interdecadal oscillationWarm CEEs increased rapidly in the past three decades, while the cold CEEs were less during this periodClimate change is the dominant factor of CEEs relative to other scale factors [ABSTRACT FROM AUTHOR]

Historical changes and possible future projections of temperature extremes in China, in terms of return values of annual extreme temperatures, are examined based on daily maximum and minimum temperatures from station observations and multiple models of the fifth and sixth phases of the Coupled Model Intercomparison Project (CMIP). The observations suggest that increases in temperature extremes are largely attributable to the changing mean climate, while the varying natural variability also has an important impact, which depends on the index of the variability. The models simulate warm extremes reasonably well but underestimate the spatial heterogeneity and temporal trend of cold extremes in China. In comparison, Coupled Model Intercomparison Project Phase 6 (CMIP6) models have higher skill in simulating temperature extremes in China, showing smaller biases and intermodel variability. MRI‐ESM2‐0 and NorESM2‐LM from CMIP6 and GFDL‐ESM2M and NorESM1‐M from CMIP5 are selected as reference models based on the better performance in reproducing observed temperature extremes in China. In the future, projections from CMIP6 multimodel ensemble (MME, represented as the multimodel median) and reference models all show a continued uptick in temperature extremes, with statistically significant increases in warm extremes mainly in the north and increases in cold extremes prominent in most parts of China. Different individual models, which have similar historical simulations, yield divergent future trends of temperature extremes, which may be associated with different climate sensitivities of models. In addition, MME usage should be treated with caution since its smoothing on spatial heterogeneity and possible information from poor models. Plain Language Summary: Given the serious hazards of extreme temperature events, understanding changes in extreme temperatures and effectively predicting their future changes are critical to climate mitigation and adaptation. According to station observations and model data from the Coupled Model Intercomparison Project Phase 5 (CMIP5) and 6 (CMIP6), we find besides the varying temperature mean, the varying temperature variance also has an important impact on changes in extreme temperatures, and CMIP6 models have overall better performance than CMIP5 models in reproducing changes in extreme temperatures in China. Our results also emphasize that good performances in historical simulation cannot guarantee better projections in the future and projections of extreme temperatures from the multimodel ensemble sacrifice the spatial heterogeneity relative to individual reference models. Key Points: The impact of the varying natural variability on changes in temperature extremes depends on the variability indexCMIP6 models perform better than CMIP5 in simulating temperature extremes changes in ChinaMultimodel ensemble projections of extreme temperatures sacrifice spatial heterogeneity [ABSTRACT FROM AUTHOR]

The Gao Fen 7 (GF‐7) satellite is China's first submeter, high‐resolution Earth observation and remote sensing satellite for natural resource monitoring, land surveying, and other industrial applications in China. The GF‐7 laser altimeter system is the successor of the ZiYuan3‐02 (ZY3‐02 satellite) laser altimeter. The key objective of the GF‐7 laser altimetry system is to provide high‐precision ground elevation control points that assist in optical imaging to achieve 1:10000 mapping in China. This paper introduces the GF‐7 laser altimeter system payload specifications, mission objectives, in‐orbit calibration plan, data products, and service policy. In the future, the GF‐7 laser altimeter system will also serve multidisciplinary and professional applications through its precise elevation measurement capabilities. In addition, it will play a vital role in Earth observation, climate change monitoring, and environmental protection. Plain Language Summary: Spaceborne laser altimeters are widely used for planet surface measurements due to their high range accuracy. This paper details the GF‐7 laser altimeter system, which is China's first formal spaceborne laser altimeter system for Earth observations. This laser altimeter system will be used in scientific experiments for the first time in China. GF‐7 laser altimeter system is the first to be equipped with laser footprint cameras and a laser optical axis surveillance camera. The GF‐7 satellite will be launched in October 2019, and the scientific experiments will begin in November. All the data it gets will be released globally. In the future, these data will help researchers from different disciplines around the world to conduct scientific experiments and monitor the Earth's resources. The GF‐7 laser altimeter system will play a vital role in global climate change monitoring, environmental protection, disaster warning, etc. The content presented in this article will help students, researchers, and engineers in the fields of geography, oceanography, and glaciology understand and use GF‐7 satellite laser data, which will be of great significance. Key Points: A new laser altimeter system design consisting of a laser altimeter, two laser footprint cameras, and a laser optical axis surveillance camera equipped with the Gao Fen 7 satellite is introducedThe application of the Gao Fen 7 laser altimeter system including ground control point measurement, polar ice sheet monitoring, and forest biomass estimation is presentedScientific experiments on the Gao Fen 7 laser altimeter system to be performed in November 2019 are given. Six levels of data products are listed and will be provided to users worldwide [ABSTRACT FROM AUTHOR]

To support future solar energy deployment in China, long‐term changes in solar energy resources over China were investigated based on high‐resolution dynamical downscaling simulations under three emission scenarios. First, an evaluation of model performance was conducted through comparison with station and ERA5 data, which indicated that although consistent overestimation was found, the spatial distribution and intra‐annual variation of solar radiation were captured well by ensemble simulation, showing added value over regions with complex topography as well. Then, the trends of the solar power output from photovoltaic (PV) systems during 2020–2099 were projected, characterized by an increase in east and central China, and a consistent decrease in the solar‐energy‐abundant regions (e.g., northeast China, the Tibetan Plateau, and northwest China) under the three scenarios. Except for under the low emission scenario, the regional averaged trends of PV were projected to decrease consistently over China in all four seasons, with significant magnitude apparent in both spring and winter. In addition, a more intense magnitude of decline was found under the higher emission scenario, with more subregions with a significant trend and higher intra‐annual and inter‐annual variability. However, it seems that the emission scenarios impact the spatial pattern only remarkably in the late century, however strengthened the decreasing magnitude in all future periods. In addition, results indicate decline in the future PV potential over most of subregions of China due to reduced radiation and increased temperature, but for the decrease over Tibetan Plateau the reduced wind speed also act as an important factor. Plain Language Summary: The long‐term goal of carbon neutrality in China involves new requirements for the development and utilization of solar energy. The present study was motivated by supporting future solar energy deployment at local scales. However, due to data limitations, relatively little effort has been made to project long‐term changes in solar energy over different subregions of China. Accordingly, we investigated this based on a set of high‐resolution dynamical downscaling simulations under different climate scenarios, and found that the higher emission scenarios reinforce the magnitude of the decline in solar energy and result in higher inter‐annual variability. These findings suggest an increased need for constraining emissions for the benefit of solar energy. In addition, we found that radiation will likely be the most important factor related to future changes in solar resources over most of subregions under RCP2.6 scenario, however the effect of wind speed should not be ignored over Tibetan Plateau. Key Points: Despite some overestimation, the spatial distribution and intra‐annual variation of solar radiation were captured well, showing added valuesFuture solar power were projected to generally increase in east and central China but decrease in solar‐energy‐abundant regionsRadiation was the most robust factor for future solar energy trend over China, however wind speed can not be ignored over Tibetan Plateau [ABSTRACT FROM AUTHOR]

The arid and semi‐arid regions are highly vulnerable to climate change and variability. Agricultural production in these regions is particularly vulnerable because of its heavy dependence on climate conditions. Therefore, it is important to improve the projections of future agro‐climatic conditions. This study investigates the changes in agroclimatic conditions for growing Chinese Medicinal Yam (CMY) in the semi‐arid North China during 2031–2050 under the Representative Concentration Pathway (RCP) 4.5 and 8.5 emission scenarios. It employs high‐resolution climate data provided by the regional climate model (RCM) of Providing Regional Climates for Impacts Studies (PRECIS‐2.0). The changes in CMY yield and producing area under future climate change are simulated by the Agro‐Ecological Zone model. The simulations are based on newly calibrated cultivar parameters. The results show that the high‐resolution RCM simulation corresponds better with the observations of precipitation and temperature than the general circulation model (GCM). Because of the increased temperature and precipitation, the agroclimatic conditions for CMY are going to improve. While the traditional yam production area will maintain suitable for CMY production, the CMY production areas will further expand northward in the future. The average yield will increase by 1,196 and 1,336 kg DM/ha under RCP 4.5 and 8.5 emission scenarios, respectively. Key Points: Improving the projections of future agro‐climatic conditions in the semi‐arid North ChinaThe high‐resolution Providing Regional Climates for Impacts Studies projections correspond better with the observationsThe Chinese Medicinal Yam production areas will expand northward in the future, the average yield will increase by 1196–1336 kg DM/ha in the future [ABSTRACT FROM AUTHOR]