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In recent years, the frequent occurrence of extreme climate disasters has seriously affected agriculture in Northeast China. Based on precipitation data derived from 83 geographical stations in the study area for 60 years (1960–2019), we chose the reliable statistical methods of the Mann–Kendall test, Sen's slope, and the Standardized Precipitation Index, and regarded drought and flood as a whole in this paper, to identify the temporal and spatial variation characteristics of precipitation and extreme climate events in Northeast China. The results show that an increasing trend of disasters was detected at the 95% confidence level (Z = 2.3). According to the timescale analysis, abrupt climate changes started in 2006. Temporal and spatial distribution of extreme climate disasters, mainly drought and flood disasters, showed a significant upward trend from 2006 to 2019. According to the spatial analysis, the precipitation in Northeast China decreased from south to north and fluctuated less from east to west. Moreover, stations with extreme climate trends (trend of climatic anomaly with confidence level > 90%) followed the same spatial pattern as those with a high frequency of extreme climate disasters (more than 17.87 times/decade). The severity and frequency of extreme climate have increasingly threatened Northeast China in the past decade. In particular, the Northeast Plain experienced the most severe and extreme climate events that seriously threatened the study area in 2007, 2009, 2010, and 2019. Our results highlight the urgent need for the development of monitoring and early warning of droughts and flood disasters to reduce economic losses. [ABSTRACT FROM AUTHOR]

In August-September 2021, the western Pacific subtropical high (WPSH) showed significant anomalies, with some areas between the Yangtze and Yellow River basins in China suffering from heavy rainfall and flooding, while extreme heat occurred in Southern China. Using the 6 h reanalysis data of NCEP/NCAR and the daily precipitation observation data, this paper studied the abnormal characteristics and the thermal mechanism of the three-dimensional ridge of the WPSH during the seasonal transition from late summer to early autumn in August-September 2021, and explored its connection with the precipitation in the areas between the Yangtze and Yellow River basins in China during the same period. The results showed that, in August-September 2021, the three-dimensional ridge of the WPSH appeared to be southward at the lower troposphere and tilted northward with height. According to the diagnosis of the atmospheric heat source, it was found that the center of atmospheric cold source in the low latitudes at 850 hPa enhanced the surface high pressure, corresponding to the WPSH ridge shifting southward at the lower troposphere, which led to more water vapor convergence and more precipitation between the Yangtze and Yellow River basins. The latent heat of condensation released by the increased precipitation between the Yangtze and Yellow River basins in China heated the middle and upper troposphere, increasing the intensity of the high pressure in the upper troposphere, corresponding to the WPSH ridge northward at the upper troposphere. This anomalous tilt of the WPSH ridge in August-September 2021 and the intense precipitation processes between the Yangtze and Yellow River basins in China during the same period established a reciprocal feedback mechanism through diabatic heating. Further studies also showed that the anomalous tilt of the WPSH ridge is one of the important modes of its three-dimensional structural anomalies, and that, in years when the ridge tilts from south to north with height anomalously in August-September, the areas between the Yangtze and Yellow River basins are characterized by high precipitation. [ABSTRACT FROM AUTHOR]

Oscillations in global modes of variability (MoVs) form global teleconnections that affect regional climate variability and modify the potential for severe and damaging weather conditions. Understanding the link between certain MoVs and regional climate can improve the ability to more accurately predict environmental conditions that impact human life and health. In this study, we explore the connection between different MoVs, including the Arctic oscillation (AO), Eurasian teleconnection, Indian Ocean dipole (IOD), North Atlantic oscillation (NAO), and El Niño southern oscillation (Nino34), with winter and summer climates in the High Mountain Asia (HMA) region, including geopotential height at 250 hPa (z250), 2 m air temperature (T2M), total precipitation (PRECTOT), and fractional snow cover area (fSCA). Relationships are explored for the same monthly period between the MoVs and the climate variables, and a lagged correlation analysis is used to investigate whether any relationship exists at different time lags. We find that T2M has a negative correlation with the Eurasian teleconnection in the Inner Tibetan Plateau and central China in both winter and summer and a positive correlation in western China in summer. PRECTOT has a positive correlation with all MoVs in most regions in winter, especially with the IOD, and a negative correlation in summer, especially with the Eurasian teleconnection. Snow cover in winter is positively correlated with most indices throughout many regions in HMA, likely due to wintertime precipitation also being positively correlated with most indices. Generally, the AO and NAO show similar correlation patterns with all climate variables, especially in the winter, possibly due to their oscillations being so similar. Furthermore, the AO and NAO are shown to be less significant in explaining the variation in HMA climate compared to other MoVs such as the Eurasian teleconnection. Overall, our results identify different time windows and specific regions within HMA that exhibit high correlations between climate and MoVs, which might offer additional predictability of the MoVs as well as of climate and weather patterns in HMA and throughout the globe. [ABSTRACT FROM AUTHOR]

Secondary ice productions (SIPs) can produce ice crystals with a number concentration much higher than that of ice nucleating particles in mixed-phase clouds and therefore influence cloud glaciation and precipitation. For midlatitude continental mesoscale convective systems (MCSs), how SIPs affect the microphysical properties and precipitation is still not clear. There are few studies of SIPs in midlatitude continental MCSs. This study investigates the roles of three SIPs (rime splintering, freezing drop shattering, and ice-ice collisional breakup) on a squall line case in North China on 18 August 2020 using the WRF model with a modified Morrison double-moment bulk microphysical scheme. Including SIPs, especially ice-ice collisional breakup, in the model simulations markedly improves the simulated convective area and convective precipitation rate of the squall line, while slightly improving the area and precipitation of the stratiform region. Within the mixed-phase layer in both the convective and stratiform regions of the squall line, ice-ice collisional breakup is the dominant process to generate ice crystals. In contrast, rime splintering generates an order of magnitude fewer ice crystals than ice-ice collisional breakup, while freezing drop shattering plays a negligible role due to the lack of large drops. Ice multiplication through ice-ice collisional breakup and rime splintering produces numerous snowflakes and graupel. This leads to enhanced depositional growth and weaker riming, which in turn weakens rime splintering. It is recommended to add SIP parameterization to the model. [ABSTRACT FROM AUTHOR]

Obtaining precise and detailed precipitation data is crucial for analyzing watershed hydrology, ensuring sustainable water resource management, and monitoring events such as floods and droughts. Due to the complex relationship between precipitation and geographic factors, this study divides the entire country of China into eight vegetation zones based on different vegetation types. Within each vegetation zone, we employ a seasonally adjusted Extreme Random Trees approach to spatially downscale GPM (Global Precipitation Measurement) satellite monthly precipitation data. To validate the effectiveness of this method, we compare it with kriging interpolation and traditional global downscaling methods. By increasing the spatial resolution of the GPM monthly precipitation dataset from 0.1° to 0.01°, we evaluate the downscaled results and validate them against ground-level rain gauge data and GPM satellite precipitation data. The results indicate that the partitioned area prediction method outperforms other approaches, resulting in a precipitation dataset that not only achieves high accuracy but also offers finer spatial resolution compared to the original GPM precipitation dataset. Overall, this approach enhances the model's capability to capture complex spatial features and demonstrates excellent generalization. The resulting higher-resolution precipitation dataset enables the creation of more accurate precipitation distribution maps, providing data support for regions lacking hydrological information. These data can be used to analyze seasonal precipitation patterns and reveal differences in precipitation across different seasons and geographic regions. [ABSTRACT FROM AUTHOR]

Global drought patterns are substantially impacted by climate change, with far-reaching implications for socioeconomic and ecological systems. Existing global climate models (GCMs) are unable to accurately project precipitation and drought characteristics, particularly in countries or regions with complex topography and significant seasonal variability, such as China. Consequently, the purpose of this study is to assess the efficacy of GCMs, and their multi-model ensemble mean, as well as to investigate the seasonal drought characteristics in China using precipitation data from CMIP6 under various "possible future" scenarios. This study selected five GCMs with historical (1961–2014) and future (2015–2100) periods, namely CNRM-CM6-1, GFDL-ESM4, MPI-ESM1-2-HR, MPI-ESM1-2-LR, and NorESM2-MM, as well as their ensemble mean ENS-CGMMN. Based on the China Daily Precipitation Analysis Product (CPAP) as the reference precipitation, the performance of these models is evaluated using the DISO index and the quantile mapping (QM) method for calibration, as well as seasonal-scale drought using the standardized precipitation index (SPI) and spatiotemporal variability analysis methods. In comparison to other climate models and the ensemble mean, the calibrated MPI-ESM1-2-HR model can more precisely describe the actual precipitation conditions at the seasonal scale. Under four scenarios, China's climate will shift from arid to moist in the future period (2015–2100) (SSP126, SSP245, SSP370, and SSP585). Autumn and summer will see a considerable increase in China's moisture levels. During the autumn, winter, and spring, the moisture will generally increase in the northern subregions of China, including the Qinghai-Tibet Plateau (QTP), Xinjiang (XJ), Northwest (NW), Northeast (NE), and North China (NC). Dryness will decrease in southern subregions, such as the Southwest (SW) and South China (SC). In contrast to these three seasons, summer in XJ exhibits a distinct trend of aridity, especially in the SSP245 scenario, whereas the NE, NC, and SC exhibit a distinct trend of moisture. To be more specific, the aridity changes in subregions during various seasons under different future climate scenarios vary significantly. This study's findings can provide significant support for future research on climate change and drought, which can help improve the accuracy of future climate projections and serve as a reference for drought risk management and policy formulation. [ABSTRACT FROM AUTHOR]

Precipitation is considered a crucial component in the hydrological cycle and changes in its spatial pattern directly influence the water resources. We compare different interpolation techniques in predicting the spatial distribution pattern of precipitation in Chongqing. Six interpolation methods, i.e., Inverse Distance Weighting (IDW), Radial Basis Function (RBF), Diffusion Interpolation with Barrier (DIB), Kernel Interpolation with Barrier (KIB), Ordinary Kriging (OK) and Empirical Bayesian Kriging (EBK), were applied to estimate different rainfall patterns. Annual mean, rainy season and dry-season precipitation was calculated from the daily precipitation time series of 34 meteorological stations with a time span of 1991 to 2019, based on Leave-One-Out Cross-Validation (LOOCV), Mean Square Error (MSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Symmetric Mean Absolute Percentage Error (SMAPE) and Nash–Sutcliffe Efficiency coefficient (NSE) as validation indexes of the applied models for calculating the error degree and accuracy. Correlation test and Spearman coefficient was performed on the estimated and observed values. A method combining Entropy Weight and Technique for Order Preference by Similarity to Ideal Solution (Entropy-Weighted TOPSIS) was introduced to rank the performance of six interpolation methods. The results indicate that interpolation technique performs better in estimating during periods of low precipitation (i.e., dry season, relative to rainy season and mean annual). The performance priorities of the six methods under the combined multiple precipitation distribution patterns are KIB > EBK > OK > RBF > DIB > IDW. Among them, KIB method has the highest accuracy which maps more accurate precipitation surfaces, with the disadvantage that estimation error is prone to outliers. EBK method is the second highest, and IDW method has the lowest accuracy with a high degree of error. This paper provides information for the application of interpolation methods in estimating rainfall spatial pattern and for water resource management of concerned regions. [ABSTRACT FROM AUTHOR]

The spring sensible heating (SSH) over the Tibetan Plateau (TP), which can significantly affect the precipitation in China, has experienced three different stages of change, showing significant increasing (1961–1979, Stage I), decreasing (1980–2002, Stage II), and increasing (2003–2014, Stage III) trends. In this study, the impact of these different trends in TP SSH on spring precipitation (SPR) in China and their possible mechanisms are investigated, based on observations and the reanalysis product. In Stage I, the SPR represents a contrasting north-south pattern associated with the increasing TP SSH, showing increasing trends over southern China and decreasing trends over central and northern China. Further, the spatial distribution of SPR trends shows a contrasting east-west pattern in Stage II. That is, persistent weakening TP SSH plays a more crucial role in increasing and decreasing SPR over southwestern and southern China, respectively. However, compared with the significant trend in SPR in Stage III, the regulation of TP SSH on SPR weakens significantly. Dynamically, the increasing TP SSH in Stage I can strengthen the subtropical westerly jet in the upper layer, simultaneously configured with an anomalous cyclone in northeastern China, which deepens the East Asian trough. Thus, anomalous convergence in the upper layer occurs over central and northern China, favoring the downdraft. It then causes more cold and dry air to move southward in the lower troposphere, which then encounters the warm and wet southwest airflows, boosting the updraft over southern China. In Stage II, regression analysis shows that if the TP SSH increases, an anomalous cyclone in the middle and upper troposphere occurs over the western TP, causing the downdraft and less precipitation over southwestern China, while a cyclone in the lower troposphere occurs over the western North Pacific and extends to southern China, promoting the ascending motions and more precipitation in southern China. However, in this stage, TP SSH actually weakens, thus contributing to more precipitation over southwestern China and less precipitation over southern China. [ABSTRACT FROM AUTHOR]

Better understanding of the spatiotemporal characteristics of precipitation is essential in developing the best management practices for ecological restoration and soil erosion control on China's Loess Plateau, an arid and semiarid region with severe soil erosion. This study aimed to explore the spatiotemporal trends of precipitation using long-term precipitation data from 1957 to 2018 from the 100 national standard meteorological stations across the Loess Plateau. The nonparametric Mann–Kendall statistical test and geospatial interpolation were used to detect the trends and to analyze spatial patterns of annual and seasonal precipitation. Wavelet analysis was applied to examine periodical variation across the plateau. The results reveal that regional annual precipitation over the Loess Plateau decreased during the 62 years of the study at a mean rate of −1.05 mm/10 years (p > 0.1). The changes in annual precipitation showed a periodical fluctuation with an increasing trend from 1957 to 1969, a decreasing trend from 1970 to 1999, and an increasing trend again in the first 18 years of the 21st century. The annual precipitation decreased in all eight sub-annual periods except for winter. Spatially, a decreasing trend occurred in the southern and eastern parts of the Loess Plateau, whereas a slight increase existed in the northwest in all periods. The decrease in six stations was statistically significant (p < 0.05), and a significant increase occurred in four stations (p < 0.1). These changes can be explained by an evident southward shift of the precipitation isohyets especially for 350 mm, 450 mm and 550 mm from the 1960s to the 1990s, and a clearly northward shift after the 1990s. Findings from this study facilitate an understanding of the spatial temporal trends of precipitation so appropriate countermeasures can be developed for effective vegetation restoration and soil erosion control across the Loess Plateau. [ABSTRACT FROM AUTHOR]

In recent years, air pollution events have occurred frequently in China and have had serious impacts on people's work and life. The research objective of the article is to find out the spatial effects of green process innovation, environmental regulation, rainfall, and pollution from industrial development on air pollution by compiling a development index of the air pollution industry and constructing spatial autoregression and spatial Durbin models. The study found that green technology innovation significantly reduced sulfur dioxide emissions and fog haze pollution. The development of industrial air pollution is an important source of sulfur dioxide and pollutant haze and has significant spatial spillover effects. The strengthening of environmental regulations leads to a decrease in life pollution but causes a transfer of industrial pollution. Rainfall is conducive to reducing the concentrations of pollutants in the air, such as those from chimneys and smog. Policy recommendations are proposed, such as establishing different environmental policies for different green technological innovations to promote green technology innovation; constructing a joint defense control system for regional air pollution; increasing the construction of artificial wetlands in areas with serious air pollution; and creating scientific plans to coordinate the development of industry, technology, and the environment, to provide help for China to reduce the emission of atmospheric environmental pollutants. [ABSTRACT FROM AUTHOR]

With global warming, previous studies have found nonuniformity responses of precipitation because of regional differences. However, climate change affects the mean, extreme, and data structure of precipitation. Quantile regression, which can reflect every part of the trends of data, was used to detect responses of each part of precipitation in China. The V2.0 dataset of daily precipitation grid data (0.5° × 0.5°) from 1961 to 2020 in China was used as practical observation data. Daily precipitation in 2015–2100 from the China Model BCC-CSM2-MR of scenarios SSP2-4.5 and SSP5-8.5 were chosen as future climate changes with moderate and high radiative forcing, respectively. On the basis of the sign consistency of the slope coefficients with quantile regression, the results of quantiles q = 0.3, 0.5, 0.7 and 0.9 were selected to represent low, median, high and flood precipitation, respectively. Precipitation in four seasons was separately analyzed to observe seasonal characteristics in China. For the observation data, precipitation had obviously different responses in the low and high percentiles and was present in mainly spring and summer. In spring, in the middle and lower Yangtze Plains, the low and median precipitation increased, whereas the high and flood precipitation significantly decreased. In summer, Heilongjiang Province and northern Inner Mongolia showed decreasing trends in the low quantile and increasing trends in the high quantile, indicating a completely opposite trend adjustment. These regions deserve more attention. However, obviously different responses in low and high percentiles were not so evident in future climate changes. Self-consistency in model data may weaken the heteroscedastic characteristics of precipitation. [ABSTRACT FROM AUTHOR]

The influence of urbanization on regional precipitation is one of the most important issues in hydrology. This paper selected the North Haihe Basin as the study area and explored the connection between summer precipitation and urbanization index (URBI) based on satellite precipitation and nighttime lights datasets. A moving spatial percentage anomaly method (MSP) was used to signify the local induced enhancement of precipitation (LIEP). Local indicators of spatial association (LISA) clustering for LIEP and URBI based on Bivariate Moran's I coupled with digital elevation model (DEM) segmentation was used to separate the impacts caused by urbanization and terrain uplifting, and thus, the regions of interest (ROI) where the urban induced enhancement of precipitation (UIEP) plays a dominant role were located. A geographically weighted regression (GWR) model was used to reveal the spatial variation of the sensitivity of UIEP to URBI within the ROI. Pearson correlation and cross-wavelet analyses (XWT) were adopted to investigate the teleconnection between UIEP and climate anomalies, using Niño3.4 and SOI as indices. The results indicated that: (1) Urbanization effects on precipitation spatial variation in the upstream mountainous area would be hidden away by topographic factors. (2) From the perspective of the basin, the highest urbanization level areas have the Lowest LIEP, while the suburban areas have the highest LIEP, and the rural areas are in the middle. (3) The UIEP and the URBI are generally negatively correlated within ROI. (4) UIEP versus Niño3.4 and SOI both show a significant common high-power period at a time scale of 2 years. This work can help comprehensively understand the hydrological response to urbanization. [ABSTRACT FROM AUTHOR]

The development of machine learning (ML) provides new means and methods for accurate climate analysis and prediction. This study focuses on summer precipitation prediction using ML algorithms. Based on BCC CSM1.1, ECMWF SEAS5, NCEP CFSv2, and JMA CPS2 model data, we conducted a multi-model ensemble (MME) prediction experiment using three tree-based ML algorithms: the decision tree (DT), random forest (RF), and adaptive boosting (AB) algorithms. On this basis, we explored the applicability of ML algorithms for ensemble prediction of seasonal precipitation in China, as well as the impact of different hyperparameters on prediction accuracy. Then, MME predictions based on optimal hyperparameters were constructed for different regions of China. The results showed that all three ML algorithms had an optimal maximum depth less than 2, which means that, based on the current amount of data, the three algorithms could only predict positive or negative precipitation anomalies, and extreme precipitation was hard to predict. The importance of each model in the ML-based MME was quantitatively evaluated. The results showed that NCEP CFSv2 and JMA CPS2 had a higher importance in MME for the eastern part of China. Finally, summer precipitation in China was predicted and tested from 2019 to 2021. According to the results, the method provided a more accurate prediction of the main rainband of summer precipitation in China. ML-based MME had a mean ACC of 0.3, an improvement of 0.09 over the weighted average MME of 0.21 for 2019–2021, exhibiting a significant improvement over the other methods. This shows that ML methods have great potential for improving short-term climate prediction. [ABSTRACT FROM AUTHOR]

In this paper, the diurnal variations of various clouds are analyzed using hourly cloud observations at weather stations in China from 1985 to 2011. In combination with merged hourly precipitation data, the relationship between the diurnal variations of clouds and precipitation in the summers from 2008 to 2011 are studied. The results show that the occurrence frequencies of total cloud and various cloud types exhibit significant diurnal variations. The diurnal variations of the occurrence frequencies of altocumulus and stratocumulus show a bimodal pattern, with peaks appearing in the early morning and late afternoon. The early morning peaks of altocumulus and stratocumulus appear earlier in the summer than in the other seasons, while the late afternoon maxima show an opposite trend. The occurrence frequency of nimbostratus peaks in the morning between 07 and 12 LST (local solar time), and the peak value lags 2 to 3 h from west to east along the Yangtze River valley; meanwhile, the diurnal variation shows no clear differences caused by changes in the latitude or seasons. Cumulus shows an afternoon (14 LST) maximum, while cumulonimbus peaks in the late afternoon during 16–20 LST, and both of them present a great diurnal range. Cirrus usually reaches its peak at 17–18 LST, and it differs by 1 to 2 h with a change in the latitude. The results of the study first show that the diurnal variations of precipitation among different regions are dominated by different clouds. The upper reaches of the Yangtze River valley present a midnight precipitation maximum that is mainly dominated by cumulonimbus. For the middle reaches of the Yangtze River valley impacted by nimbostratus, the precipitation peaks in the early morning. In South and Northeast China, the precipitation peaks in the afternoon and is determined by the diurnal variations of convective clouds. In the region between the Yangtze River valley and Yellow River valley, the precipitation peaks in the early morning and afternoon; the early morning peak is mainly determined by stratiform clouds, while the afternoon peak is closely related to convective clouds. [ABSTRACT FROM AUTHOR]

Drought is one of the major environmental stressors; drought is increasingly threatening the living environment of mankind. The standardized precipitation evapotranspiration index (SPEI) with a 12-month timescale was adopted to monitor dry–wet status over China from 1951 to 2021. The modified Mann–Kendall (MMK) and Pettitt tests were used to assess the temporal trend and nonlinear behavior of annual drought variability. The analysis focuses on the spatio-temporal structure of the dry–wet transition and its general connections with climate change processes. In addition, the seasonal autoregressive integrated moving average (SARIMA) model was applied to forecast the dry–wet behavior in the next year (2022) at 160 stations, and the hotspot areas for extreme dryness–wetness in China were identified in the near term. The results indicate that the dry–wet climate in China overall exhibits interannual variability characterized by intensified drought. The climate in the Northeast China (NEC), North China (NC), Northwest China (NWC), and Southwest China (SWC) has experienced a significant (p < 0.05) drying trend; however, the dry–wet changes in the East China (EC) and South Central China (SCC) are highly spatially heterogeneous. The significant uptrend in precipitation is mainly concentrated to the west of 100° E; the rising magnitude of precipitation is higher in Eastern China near 30° N, with a changing rate of 20–40 mm/decade. Each of the sub-regions has experienced significant (p < 0.01) warming over the past 71 years. Geographically, the increase in temperature north of 30° N is noticeably higher than that south of 30° N, with trend magnitudes of 0.30–0.50 °C/decade and 0.15–0.30 °C/decade, respectively. The response of the northern part of Eastern China to the warming trend had already emerged as early as the 1980s; these responses were earlier and more intense than those south of 40° N latitude (1990s). The drying trends are statistically significant in the northern and southern regions, bounded by 30° N, with trend magnitudes of −0.30–−0.20/decade and −0.20–−0.10/decade, respectively. The northern and southwestern parts of China have experienced a significant (p < 0.05) increase in the drought level since the 1950s, which is closely related to significant warming in recent decades. This study reveals the consistency of the spatial distribution of variations in precipitation and the SPEI along 30° N latitude. A weak uptrend in the SPEI, i.e., an increase in wetness, is shown in Eastern China surrounding 30° N, with a changing rate of 0.003–0.10/decade; this is closely associated with increasing precipitation in the area. Drought forecasting indicates that recent drying areas are located in NWC, the western part of NC, the western part of SWC, and the southern part of SCC. The climate is expected to show wetting characteristics in NEC, the southeastern part of NC, and the eastern part of EC. The dry–wet conditions spanning the area between 30–40° N and 100–110° E exhibit a greater spatial variability. The region between 20–50° N and 80–105° E will continue to face intense challenges from drought in the near future. This study provides compelling evidence for the temporal variability of meteorological drought in different sub-regions of China. The findings may contribute to understanding the spatio-temporal effect of historical climate change on dry–wet variation in the region since the 1950s, particularly in the context of global warming. [ABSTRACT FROM AUTHOR]

A significant greening trend has been reported globally in recent decades. The greening indicates the improvement in net primary production (NPP) in vegetation. Adopting statistics-based regression models, we investigated the dynamics of NPP and its climatic drivers in main ecosystems (forest land, grass land, and unused land) over China during the period 2000–2021. The results confirmed an increasing NPP covering approximately 86% area in the main ecosystems. NPP exhibited an increase rate of 6.11 g C m−2 yr−1 in forest land, 4.77 g C m−2 yr−1 in grass land, and 1.25 g C m−2 yr−1 in unused land, respectively. Over the same period, warm–wet climate trend was observed covering approximately 90% of the main ecosystems. The warm–wet climate has had a positive effect rather than negative effect on NPP in the main ecosystems, judging by their significant positive correlation. Our results suggested that the increase in annual precipitation exerted much more important effect on the increasing NPP. The warm–wet climate trend contributes to the upward trend in NPP, even if variability in NPP might involve the influence of solar radiation, atmospheric aerosols, CO2 fertilization, nitrogen deposition, human intervention, etc. [ABSTRACT FROM AUTHOR]

On 16 September 2018, the Yangtze River Delta (YRD) experienced heavy precipitation, with the local daily precipitation exceeding 250 mm. Using ERA5 reanalysis data and satellite observations from the GPM, we review this heavy rain event in terms of its meteorological triggers and water vapor transport. As the high-level water vapor produced by Typhoon Mangkhut continued to be transported northward, the precipitation in the YRD gradually increased, and stratus precipitation played a major role in this event. The high-level water vapor continued to be transported northward to the east of Taiwan Island without falling, so heavy precipitation did not appear to the east of Taiwan Island. In the present study, we suggest that the meteorological trigger of this event was mainly the gradual falling of ice particles moving northward from a high altitude. The high-level ice particles originated from the "water tower" at the center of Typhoon Mangkhut, which pumped low-level water vapor into the high-level water vapor. In general, the appearance of abnormal values of high-level water vapor transport is an important atmospheric disturbance related to heavy precipitation in the downstream areas of high-level wind, and the typhoon water tower can be used as an important forecast signal for long-distance heavy precipitation in China during the active typhoon period. [ABSTRACT FROM AUTHOR]

Xinjiang is located in an arid and semi-arid climate region in China, but Xinjiang Ili river valley is more humid, with higher precipitation intensity and precipitation, which is closely related to the role of the Tianshan Mountains. In this paper, through the NCRP 1° × 1° reanalysis data and the conventional observation data of the Ili River Valley in Xinjiang, the terrain sensitivity experiment conducted by the WRF model is used to analyze the short-term extreme precipitation event of the Ili River Valley from 18–19 of May 2017, to reveal the influence of Tianshan Mountains on the extreme precipitation event of the Ili River Valley. The results show that: (1) The reduction or removal of the terrain will cause a wide range of wind field changes, weaken the vertical upward movement of the windward slope, and the accumulation of water vapor before the windward slope will also be reduced; a large-scale change of the terrain will also affect the direction of water vapor transportation. These effects together lead to a decrease or increase in regional precipitation. (2) "Fuzzy" (smooth) terrain will affect the precipitation simulated by changing the local vertical movement and water vapor transport, which shows that the WRF model's accurate description of the terrain structure characteristics of mountainous areas is beneficial to accurately simulate the precipitation process on the windward slope area. [ABSTRACT FROM AUTHOR]

The machine learning algorithms application in atmospheric sciences along the Earth System Models has the potential of improving prediction, forecast, and reconstruction of missing data. In the current study, a combination of two machine learning techniques namely K-means, and decision tree (C4.5) algorithms, are used to separate observed precipitation into clusters and classified the associated large-scale circulation indices. Observed precipitation from the Chinese Meteorological Agency (CMA) during 1961–2016 for 83 stations in the Poyang Lake basin (PLB) is used. The results from K-Means clusters show two precipitation clusters splitting the PLB precipitation into a northern and southern cluster, with a silhouette coefficient ~0.5. The PLB precipitation leading cluster (C1) contains 48 stations accounting for 58% of the regional station density, while Cluster 2 (C2) covers 35, accounting for 42% of the stations. The interannual variability in precipitation exhibited significant differences for both clusters. The decision tree (C4.5) is employed to explore the large-scale atmospheric indices from National Climate Center (NCC) associated with each cluster during the preceding spring season as a predictor. The C1 precipitation was linked with the location and intensity of subtropical ridgeline position over Northern Africa, whereas the C2 precipitation was suggested to be associated with the Atlantic-European Polar Vortex Area Index. The precipitation anomalies further validated the results of both algorithms. The findings are in accordance with previous studies conducted globally and hence recommend the applications of machine learning techniques in atmospheric science on a sub-regional and sub-seasonal scale. Future studies should explore the dynamics of the K-Means, and C4.5 derived indicators for a better assessment on a regional scale. This research based on machine learning methods may bring a new solution to climate forecast. [ABSTRACT FROM AUTHOR]

The Heihe River Basin (HRB), located on the northeastern edge of the Tibetan Plateau, is the second-largest inland river basin in China, with an area of 140,000 km2. The HRB is a coupling area of the westerlies, the Qinghai–Tibet Plateau monsoon and the Southeast monsoon circulation system, and is a relatively independent land-surface water-circulating system. The refined characteristics of moisture recycling over the HRB was described by using the Weather Research and Forecasting (WRF) model for a long-term simulation, and the "finer box model" for calculating the net water-vapor flux. The following conclusions were drawn from the results of this study: (1) The water vapor of the HRB was dominantly transported by the wind from the west and from the north, and the west one was much larger than the north one. The net vapor transported by the west wind was positive, and by the north wind was negative. (2) The precipitation over the HRB was triggered mainly by the vapor from the west, which arose from the lower vertical layer to higher one during transporting from west to east. The vapor from the north sank from a higher layer to a lower one, and crossed the south edge of the HRB. (3) The moisture-recycling ratio of evapotranspiration to precipitation over the HRB was much higher than the other regions, which may be due to the strong land–atmosphere interaction in the arid inland river basin. [ABSTRACT FROM AUTHOR]

Although much progress has been made in identifying the large-scale drivers of recent summer precipitation variability in North China, the evolution of these drivers over longer time scales remains unclear. We investigate multidecadal and interannual variability in North China summer precipitation in the 110-year Coupled ECMWF Reanalysis of the Twentieth Century (CERA-20C), considering changes in regional moisture and surface energy budgets along with nine circulation indices linked to anomalous precipitation in this region. The CERA-20C record is separated into three distinct periods according to the running climatology of summer precipitation: 1901–1944 (neutral), 1945–1979 (wet), and 1980–2010 (dry). CERA-20C reproduces expected relationships between large-scale drivers and regional summer precipitation anomalies well during 1980–2010, but these relationships generally do not extend to earlier periods. For example, a strong relationship with the Eurasian teleconnection pattern only emerges in the late 1970s, while correlations with the El Niño-Southern Oscillation and the Pacific–Japan pattern change sign in the mid-twentieth century. We evaluate two possible reasons for this nonstationarity: (1) the underlying atmospheric model may require strong data assimilation constraints to capture large-scale circulation influences on North China, or (2) large-scale drivers inferred from recent records may be less general than expected. Our analysis indicates that both factors contribute to the identified nonstationarity in CERA-20C, with implications for the reliability of seasonal forecasts and climate projections based on current models. [ABSTRACT FROM AUTHOR]

Atmospheric nitrogen deposition is of great concern to both air quality and the ecosystem, particularly in northern China, which covers one-quarter of China's cultivated land and has many heavily air polluted cities. To understand the characteristics of wet N deposition at rural sites in northern China, one-year wet deposition samples were collected in the Daheitin reservoir region. Due to the intense emissions of gaseous nitrogen compounds from heating activities during cold seasons and distinct dilution effects under different rainfall intensities and frequencies, the volume weighted mean concentrations of wet N deposition showed higher levels in dry seasons but lower levels in wet seasons. In contrast, the wet N deposition rates varied consistently with precipitation, i.e., high during the wet season and lower during the dry season. The annual wet deposition rate of total inorganic ions (the sum of NO3−–N and NH4+–N) at the rural site in North China from July 2019 to June 2020 was observed at 18.9 kg N ha−1 yr−1, still remained at a relatively high level. In addition, biomass burning activities are ubiquitous in China, especially in northern China; however, studies on its impact on wet N deposition are limited. Non-sea salt potassium ion (nss-K+) was employed as a molecular tracer to investigate the characteristics of biomass burning activities as well as their impact on the chemical properties of wet N deposition. Three precipitation events with high nss-K+ levels were captured during the harvest season (June to July). The variations in the patterns of nss-K+, deposited N species, and ratios of nss-K+ to nitrogen species as well as their relationships all indicated that biomass burning emissions contributed remarkably to NO3−–N but had a minor influence on NH4+–N. [ABSTRACT FROM AUTHOR]

Air temperature and precipitation are two important meteorological factors affecting the earth's energy exchange and hydrological process. High quality temperature and precipitation forcing datasets are of great significance to agro-meteorology and disaster monitoring. In this study, the accuracy of air temperature and precipitation of the fifth generation of atmospheric reanalysis produced by the European Centre for Medium-Range Weather Forecasts (ERA5) and High-Resolution China Meteorological Administration Land Data Assimilation System (HRCLDAS) datasets are compared and evaluated from multiple spatial–temporal perspectives based on the ground meteorological station observations over major land areas of China in 2018. Concurrently, the applicability to the monitoring of high temperatures and rainstorms is also distinguished. The results show that (1) although both forcing datasets can capture the broad features of spatial distribution and seasonal variation in air temperature and precipitation, HRCLDAS shows more detailed features, especially in areas with complex underlying surfaces; (2) compared with the ground observations, it can be found that the air temperature and precipitation of HRCLDAS perform better than ERA5. The root-mean-square error (RMSE) of mean air temperature are 1.3 °C for HRCLDAS and 2.3 °C for ERA5, and the RMSE of precipitation are 2.4 mm for HRCLDAS and 5.4 mm for ERA5; (3) in the monitoring of important weather processes, the two forcing datasets can well reproduce the high temperature, rainstorm and heavy rainstorm events from June to August in 2018. HRCLDAS is more accurate in the area and magnitude of high temperature and rainstorm due to its high spatial and temporal resolution. The evaluation results can help researchers to understand the superiority and drawbacks of these two forcing datasets and select datasets reasonably in the study of climate change, agro-meteorological modeling, extreme weather research, hydrological processes and sustainable development. [ABSTRACT FROM AUTHOR]

Atmospheric aerosol pollution has significant impacts on human health and economic society. One of the most efficient way to remove the pollutants from the atmosphere is wet deposition. This study selected three typical atmospheric pollution regions in China, the Beijing-Tianjin-Hebei (BTH), the Yangtze River Delta (YRD) and the Pearl River Delta (PRD) regions, as research areas, and used the hourly precipitation and PM2.5 mass concentration data from 2015 to 2017 to investigate the removal impacts of precipitation on PM2.5. The PM2.5 mass concentration difference before and after the hourly precipitation events was used to denote as the impacts of precipitation. Hourly precipitation event was selected so that the time difference between two PM2.5 observations was short enough to limit the PM2.5 change caused by other factors. This study focused on the differences in the removal effect of precipitation on PM2.5 under different precipitation intensities and pollution levels. The results show that both precipitation intensity and aerosol amount affected the removal effect. A negative removal effect existed for both light precipitation and low PM2.5 mass concentration conditions. In contrast, a positive removal effect occurred for both high precipitation and high PM2.5 mass concentration conditions. The removal effect increased with increasing precipitation intensity and PM2.5 mass concentration before precipitation and was consistent with the change trend of wind speed at a height of 100 m. The findings of this study can help understand the mechanism of wet scavenging on air pollution, providing support for air pollution control in future. [ABSTRACT FROM AUTHOR]

Using composite, regular, and partial regression analyses in the six consecutive seasons from spring of the El Niño–Southern Oscillation (ENSO)-/Indian Ocean Dipole (IOD)-developing year through summer following the ENSO/IOD mature phase, the individual and combined impacts of El Niño and positive Indian Ocean Dipole (pIOD) on the evolution of precipitation in China are diagnosed for the period 1950–2013. It is shown that the seasonal responses of precipitation in China to El Niño and pIOD events, and their relationship with the large-scale atmospheric circulations, differ from one season to another. For the pure El Niño years, there is a seasonal reversal of precipitation over southeastern and northwestern China, with deficient precipitation occurring in these two regions before the onset of anomalous wet conditions in the developing autumn. Meanwhile, North China tends to be drier than normal in the developing seasons, but wetter than normal in the decaying seasons. For the pure pIOD events, southern China suffers a precipitation deficit (surplus) in the developing spring (summer and autumn). Furthermore, both North China and northwestern China experience excessive precipitation in the developing autumn and decaying summer. In addition, there is reduced precipitation in northeastern China during both the developing and decaying summers, whereas increased precipitation occurs in the developing autumn and decaying winter. For the combined years, southern China experiences enhanced moisture supply and suffers from increased precipitation from the developing summer through the subsequent spring, but reduced precipitation in the developing spring and decaying summer. Similar to the pure El Niño, northwestern (North) China becomes wetter than normal after the developing summer (autumn) in the combined years. In general, the ENSO/IOD-related precipitation variability could be explained by the associated anomaly circulations. [ABSTRACT FROM AUTHOR]

Hubei Province Region (HPR), located in Central China, is a concentrated area of severe convective weather. Three severe convective processes occurred in HPR were selected, namely 14–15 May 2015 (Case 1), 6–7 July 2013 (Case 2), and 11–12 September 2014 (Case 3). In order to investigate the differences between the three cases, the temporal and spatial distribution characteristics of cloud–ground lightning (CG) flashes and precipitation, the distribution of radar parameters, and the evolution of cloud environment characteristics (including water vapor (VD), liquid water content (LWC), relative humidity (RH), and temperature) were compared and analyzed by using the data of lightning locator, S-band Doppler radar, ground-based microwave radiometer (MWR), and automatic weather stations (AWS) in this study. The results showed that 80% of the CG flashes had an inverse correlation with the spatial distribution of heavy rainfall, 28.6% of positive CG (+CG) flashes occurred at the center of precipitation (>30 mm), and the percentage was higher than that of negative CG (−CG) flashes (13%). Moreover, the quantity of thunderstorm cells in Case 1 was more than other cases, the peak time of +CG flashes was prior to that of total CG flashes in Case 2 and Case 3, and the time of +CG flashes' peak in Case 2 was prior to that of precipitation at about 2 h. Based on the analysis of the cloud environment, there are three main reasons for the differences of CG flashes and precipitation. Firstly, the structure of the LWC vertical profile and the height of the LWC peak are different, and high LWC makes it difficult for the collision of ice particles to generate electricity. Secondly, the differences between convective available potential energy (CAPE), precipitation, and CG flashes is caused by the sudden increase of VD from 1.5 km to 3 km, and thirdly, the production of CG flashes is very sensitive to RH at the surface layer and the total CG flashes increase as the RH increasing. [ABSTRACT FROM AUTHOR]