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The Northeastern China cold vortex (NCCV) is one type of strong cyclonic vortex that occurs near Northeastern China (NEC), and NCCV activities are typically accompanied by a series of hazardous weather. This paper employed an automatic algorithm to identify the NCCVs from 1979 to 2018 and analyzed their circulation patterns and climatic impacts by using the defined NCCV intensity index (NCCVI). The analysis revealed that the NCCV activities in summer exhibited a strong inter-annual variability, with an obvious periodicity of 3-4 years and 6-7 years, but without significant trends. In years when the NCCVI was high, NEC experienced negative geopotential height anomalies, cyclonic circulation, and cooler temperature anomalies, which were conducive to the maintenance and development of NCCV activities. Furthermore, large amounts of water vapor converged in NEC through two transportation routes as the NCCVs intensified, leading to a significant positive (negative) correlation with the summer precipitation (surface temperature) in NEC. The Atlantic sea surface temperature (SST) anomalies were closely related to summer NCCV activities. As the Atlantic SST rose, large amounts of surface sensible and latent heat flux were transported into the lower troposphere, inducing a positive geopotential height anomaly that occurred on the east side of the heat source. As a result, an eastward diverging flow was formed in the upper troposphere and propagated downstream, i.e., the eastward propagating Rossby wave train, which eventually led to a coupled circulation in the Ural Mountains and NEC, as well as more intensive NCCV activities in summer. [ABSTRACT FROM AUTHOR]

Moisture sources and transport processes play a critical part in hydrological cycle and determine regional precipitation. This paper utilizes the Water Accounting Model‐2layers (WAM‐2layers) and the ERA5 reanalysis data to track the sources of precipitation over the Pearl River Basin (PRB). The contribution of external moisture and the role of local recycling are investigated. The results show that during the period from 1980 to 2020, oceanic sources including the western North Pacific and Indian Oceans serve as the primary moisture sources of precipitation over the PRB. The contributions to total seasonal precipitation are respectively 62.57% in MAM, 54.79% in JJA, 43.70% in SON and 60.88% in DJF. By contrast, the contribution of local recycling is generally below 5.50%. In the dry years of 1994, 1997 and 2001, the contribution of terrestrial sources is about 19.22%; in the wet years of 1989, 2009 and 2011, the contribution is about 16.31%. The summer precipitation anomalies are mainly attributable to moisture anomalies from the Equatorial Indian Ocean in the wet years and from Southeast Asia in the dry years. Furthermore, vertically integrated moisture flux anomalies over the boundaries of the PRB are generally the result of anomalous wind rather than anomalous moisture. In the wet years, low‐pressure systems induce strong cyclonic moisture transports, increasing the PRB precipitation. In the dry years, high‐pressure anomalies over the PRB block the moisture transports from the Indian Ocean and western North Pacific. [ABSTRACT FROM AUTHOR]

This study assesses the midsummer precipitation prediction over eastern China by the dynamic downscaling method. Based on the Climate Forecast System version 2 of the National Centers for Environmental Prediction and the Weather Research and Forecasting Model, the prediction performance of global and regional models on the July precipitation over eastern China is further analyzed by hindcast experiments from 1982 to 2010 and prediction experiments from 2011 to 2021. The results suggest that the regional model forced by the global model can noticeably improve the prediction skill for precipitation in eastern China, especially in the region from the South of North China to the Yangtze River Basin, referred as the northern China in this paper. In addition, we perform a diagnostic analysis of the reason for the improvement of the model prediction skill. The results indicate that the high resolution of the regional model and the refinement of physical process parameterizations contribute to improving the simulation ability for the East Asian atmospheric circulation pattern, heat flux, especially for the meridional teleconnection pattern in East Asia and the sensible heat flux in the northern China, thus further improving precipitation prediction. [ABSTRACT FROM AUTHOR]

Karst spring water dynamic characteristics and its response to atmospheric precipitation are of great significance for water resources utilization under the background of climate change. This paper selects Longzici spring area, North China, as the study area. Based on a long series of spring water flow and precipitation data, the dynamic characteristics of spring flow were analyzed and the numerical simulation of the groundwater flow model was established. The results show that the groundwater kept the sustained decline over the past decades which is in a negative equilibrium state, with a storage variable of − 2.26 million m3/year. The sensitivity of spring flow to precipitation under different precipitation scenarios shows that the water level changes in the recharge and drainage areas are similar about (3–5 cm) and slightly larger than that in the runoff area(1.5 cm) when minimum rainfall (287.24 mm) happens. When the precipitation is at its maximum (867.66 mm), the water level change in the runoff area can reach 95 cm which is much larger than those in the recharge and discharge areas. The results indicate that Longzici karst spring has a relatively good regulation water resource capacity and the runoff area is more sensitive which plays an important role in response to climate change. [ABSTRACT FROM AUTHOR]

Concurrent compound dry and hot events (CDHEs) amplified more damange on the ecosystems and human society than individual extremes. Under climate change, compound dry and hot events become more frequent on a global scale. This paper proposes a mathematical method to quantitatively attribute changes of CDHEs to changes of precipitation, change in temperature and change in the dependence between precipitation and temperature. The attribution is achieved by formulating the total differential equation of the return period of CDHEs among Meta-gaussian model. A case study of China is devised based on monthly precipitation and temperature data during the period from 1921 to 2020 for 80 major river basins. It is found that temperature is the main driving factor of increases in CDHEs for 49 major river basins in China, except for the upper and middle reaches of the Yangtze River. In West China, precipitation changes drove the increase in CDHEs in 18 river basins (23%), particularly in parts of North Xinjiang, Qinghai and Gansu. On the other hand, dependence between precipitation and temperature dominated changes of CDHEs in 13 river basins (16%) of China with other factors, including parts of South China, East China and Northwestern China. Furthermore, changes in both the mean and spread of precipitation and temperature can also contribute to changes in CDHEs. [ABSTRACT FROM AUTHOR]

Changes in patterns of meteorological parameters, like precipitations, temperature, wind, etc., are causing significant increases in various extreme events. And these extreme events, i.e., floods, heatwaves, hurricanes, droughts, etc., lead to a shortage of water resources, crop failures, wildfires, and economic losses. However, Global Circulation Models (GCMs) are considered the most important tools for quantifying climate change. Therefore, we selected 20 different GCMs of precipitation in our research, as the frequency of extreme events, like drought and flood, is highly related to changes in precipitation patterns. However, this research introduced a new weighting scheme — MCFSAWS-Ensemble: Monte Carlo Feature Selection Adaptive Weighting Scheme to Ensemble multiple GCMs, whereas, Monte Carlo Feature Selection (MCFS) is one of the most popular algorithms for discovering important variables. However, the proposed weighting scheme (MCFSAWS-Ensemble) is mainly based on two sources. Initially, it evaluates the prior performance of each GCM model to define their relative importance using MCFS. Then, it computes value by value difference between the observed and simulated model. In addition, the application of this paper is based on the monthly time series data of precipitation in the Tibet Plateau region of China. In addition, we used twenty GCMs from the Coupled Model Intercomparison Project Phase 6 (CMIP6) to analyze the implications of the MCFSAWS-Ensemble. Further, we compared the performance of the MCFSAWS-Ensemble scheme with Simple Model Averaging (SMA) through Mean Average Error (MAE) and correlation statistics. The results of this research indicate that the proposed weighting scheme (MCFSAWS-Ensemble) is more accurate than the SMA approach. Consequently, we recommend the use of advanced machine learning algorithms such as MCFS for making accurate multi-model ensembles. [ABSTRACT FROM AUTHOR]

Seasonal precipitation and temperature predictions with global climate models that are developed based on the ocean–atmosphere interactions, contribute to the water resources management and hazard mitigation. To date, multi-model ensemble seasonal climate prediction systems, such as North American multi-model ensemble (NMME), are an effective way to provide useful forecast information a few months ahead especially over regions with strong ocean–atmosphere coupling. Previous studies have evaluated the skill of NMME hindcasts worldwide, however, whether the NMME real-time forecasts perform as well as the hindcasts and how the decadal variability in ocean-atmospheric teleconnections affect the prediction skill remain unclear. In this paper, based on precipitation and temperature datasets from nine models of NMME, the evaluation of forecast skills during hindcast (1982–2010) and real-time forecast (2011–2020) periods is carried out in the Yangtze River basin over China. Results show that although selecting an appropriate time frame for the calculation of climatology can reduce errors of real-time prediction, but the real-time prediction skills are lower than hindcast skills in the Yangtze River basin, with anomaly correlation decreased by 14–51% (38–75%) and error increased by 30–31% (51–55%) for seasonal precipitation (temperature) predictions up to the sixth lead-season, and the skill decrease is larger at longer leads. The failure in representing the decadal variations of ocean-atmospheric teleconnection (especially the association with Indian Ocean surface temperature) during the real-time forecast period can partly explain the decline in the prediction skills. Our findings suggest that a better grasp of decadal variability is needed to improve the real-time climate predictions. [ABSTRACT FROM AUTHOR]

Inland flooding and mudslides from tropical cyclone (TC) rainstorms are among the most destructive natural hazards in China, resulting in considerable direct economic losses and large numbers of fatalities. In this paper, a TC precipitation model (TCPM) is improved by incorporating the effects of complex terrain through a set of new parameters (e.g., slope, roughness, and attenuation distance) for a more accurate assessment of TC rainfall hazards in China. Moreover, by introducing parameterized spiral rainbands, the model could more accurately capture the intensity of extreme precipitation. The model comprehensively considers dynamic and thermodynamic precipitation factors and is adept at capturing the climate characteristics of TC precipitation and the probability distribution of extreme TC precipitation in China. The model is verified by providing two comparisons. One is analysis including detailed results of three typical TC cases, and the other uses empirical cumulative distribution functions for extreme observations and simulations of historical landfalling TCs in China during the period 1960–2018. The comparisons reveal that the TCPM shows impressive performance for strong TCs with heavy precipitation within 200–300 km of the TC center. Moreover, both the modeled extreme hourly and total TC precipitation probability distributions are consistent with the observations. However, the model needs to be further improved for TCs with dispersive or long-distance precipitation. Significance Statement: In this paper, an optimized and physics-based model for the simulation of tropical cyclone precipitation is described and used to estimate the risk of TC rainfall hazards in China. The work is innovative in that it considers the effect of complex terrain from three perspectives, including slope, roughness, and attenuation distance. The simulations demonstrated that the model is adept at capturing the main climate characteristics of TC precipitation and the probability distribution of extreme TC precipitation in China, which is simple to run several hundred thousand times, with bright application prospects in catastrophe risk assessment. [ABSTRACT FROM AUTHOR]

In this paper, we describe and analyze two datasets entitled "Homogenised monthly and daily temperature and precipitation time series in China during 1960–2021" and "Homogenised monthly and daily temperature and precipitation time series in Greece during 1960–2010". These datasets provide the homogenised monthly and daily mean (TG), minimum (TN), and maximum (TX) temperature and precipitation (RR) records since 1960 at 366 stations in China and 56 stations in Greece. The datasets are available at the Science Data Bank repository and can be downloaded from https://doi.org/10.57760/sciencedb.01731 and https://doi.org/10.57760/sciencedb.01720. For China, the regional mean annual TG, TX, TN, and RR series during 1960–2021 showed significant warming or increasing trends of 0.27°C (10 yr)−1, 0.22°C (10 yr)−1, 0.35°C (10 yr)−1, and 6.81 mm (10 yr)−1, respectively. Most of the seasonal series revealed trends significant at the 0.05 level, except for the spring, summer, and autumn RR series. For Greece, there were increasing trends of 0.09°C (10 yr)−1, 0.08°C (10 yr)−1, and 0.11°C (10 yr)−1 for the annual TG, TX, and TN series, respectively, while a decreasing trend of −23.35 mm (10 yr)−1 was present for RR. The seasonal trends showed a significant warming rate for summer, but no significant changes were noted for spring (except for TN), autumn, and winter. For RR, only the winter time series displayed a statistically significant and robust trend [−15.82 mm (10 yr)−1]. The final homogenised temperature and precipitation time series for both China and Greece provide a better representation of the large-scale pattern of climate change over the past decades and provide a quality information source for climatological analyses. [ABSTRACT FROM AUTHOR]

Rainfall and wind speed are two important meteorological variables that have a significant impact on agriculture, human health, and socioeconomic development. While individual rainfall or wind events have been widely studied, little attention has been devoted to studying the lead-lag relationship between rainfall and wind speed, particularly in coastal regions where strong dependence between rainfall and wind speed is expected. Taking China's coastline as the case study, this paper aims to explore the variation trends of wind speed and rainfall and reveal the relationships between rainfall events and wind speeds on days before and after rainfall occurrence, by using meteorological station data from 1960 to 2018. The results show that wind speed tended to decrease while rainfall showed a slight increase for most stations. The daily wind speed increased 2 days before rainfall occurrence and decreased after then, with the highest wind speed observed during rainfall onset regardless of rainfall amount. Moreover, heavier rainfall events are more likely to occur with higher wind speeds. The findings of this study potentially improve the understanding of the dependence of rainfall and wind speed, which could help rainfall or wind-related disaster mitigation. [ABSTRACT FROM AUTHOR]

Increasing variability of precipitation induced by climate change raises uncertainties for ecosystem services and agricultural production in the Poyang Lake Basin (PLB), China. Reanalysis precipitation datasets (RPDs) are attractive alternatives for monitoring the hydro‐climate cycles. However, their applicability in China has mainly been evaluated at the national scale, while less attention has been paid to the PLB or southeastern China. Here, we apply various metrics to evaluate the magnitude and spatial similarity of annual, monthly, different intensities and the spatial structure of precipitation from four reanalysis—the fifth global atmospheric analysis dataset of the European Center for Medium‐Range Weather Forecasts (ECMWF) (ERA5), Interim ECMWF Reanalysis (ERA‐Interim), Japanese 55‐year Reanalysis project (JRA55) and Modern‐Era Retrospective analysis for Research and Applications, Version 2 (MERRA‐2), against the rain‐gauges over the PLB during 1980–2018. The results showed that the detection capacity of ERA5 and MERRA2 shifted around 2002, whereas that of ERA‐Interim and JRA55 did not substantially change. Before 2002, ERA‐Interim had the lowest biases for the magnitude of annual, monthly and different intensities of precipitation (except for heavy rain), while ERA5 and MERRA2 had strengths in capturing spatial correlation. Notably, ERA5 and MERRA2 had region‐wide wet and dry biases, respectively, before 2002, which can be attributed mainly to the estimation of flood season precipitation (from April to June) and heavy rain. In contrast, the fractional contribution of light–moderate rain was underestimated and overestimated by ERA5 and MERRA2, respectively. However, after 2002, because of the improved reproducibility of flood season precipitation and heavy rain magnitude, the systematic biases of ERA5 and MERRA2 significantly decreased, especially for ERA5, which outperformed other RPDs on all aspects of precipitation. The conclusions of this paper suggest that the variation of applicability over time should be seriously tested before the utilization of RPDs in hydro‐climate analyses. [ABSTRACT FROM AUTHOR]

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]

Quantitatively evaluation of satellite precipitation and potential evapotranspiration products with the high resolution, wide coverage and dynamic continuity is an important foundation for drought monitoring over mainland China. This paper examined the accuracy of products and the utility of drought monitoring using the newly released long-term (more than 30 years) satellite precipitation products (climate hazards group infrared precipitation with station, CHIRPS) data version 2.0 and potential evapotranspiration products (global land-surface evaporation: the Amsterdam methodology, GLEAM) dataset. We selected standardized precipitation evapotranspiration index (SPEI) and six statistical and classification measures indexes to evaluate accuracy and the utility of CHIRPS and GLEAM-Ep (potential evapotranspiration derived from GLEAM), with China Gauge-based Precipitation Daily Analysis dataset (CGDPA) and potential evapotranspiration calculated by Penman-Monteith (PM-Ep) as evaluation reference data sets. The results showed that CHIRPS and GLEAM-Ep performed well in eastern and southwest of China. However, their performance in western China was uncertain due to the sparsity of meteorological stations. SPEIs (SPEI value calculated by the CHIRPS and GLEAM-Ep) had a good performance on drought monitoring in eastern and southwestern China. Values of correlation coefficient (CC) between SPEIs and SPEIm (SPEI value calculated by the CGDPA and PM-Ep) were more than 0.6 in southeastern China, southwestern China, the middle and lower reaches of the Yangtze River region and northern China, especially in relatively humid areas, which the correlation coefficient values were more than 0.7 and the root mean-squared error (RMSE) values were less than 0.8. In order to further evaluate the performance of SPEIs in drought monitoring at spatial and temporal scales, the SPEIs3 (SPEIs at three-month scale) and SPEIm3 (SPEIm at three-month scale) in southeastern China, southwestern China, the middle and lower reaches of the Yangtze River region and northern China were compared. SPEIs3 had a better simulation effect on the evolution process of the ratio of drought area in those regions during1981-2016. And the success ratio of simulating drought duration was about 0.93, 0.57, 0.71 and 0.81, respectively. Moreover, SPEIs3 displayed a high accuracy over the four regions in capturing drought, which was the spatial distribution of typical drought events on specific month. In conclusion, SPEI based on long-term CHIRPS and GLEAM-Ep could be applied to monitor and evaluate drought in eastern and southwestern China. This paper can provide reliable reference for monitoring and warning of drought events and water resources management in those regions. [ABSTRACT FROM AUTHOR]

• Significantly changes of LAI and GPP mostly occurred in eastern and southern China. • Divergence between LAI and GPP were found in regions with dense vegetation coverage. • The importance of VPD, SM, and PT to vegetation indices increased as latitude decreases. Climate change notably boosts terrestrial hydraulic redistribution pattern, which could directly affect the spatiotemporal evolution characteristics of vegetation productivity. However, the spatiotemporal evolution pattern of long-term hydrometeorological controls of vegetation productivity is still not fully understood. This paper comprehensively explored the impacts of hydrometeorological controls on leaf area index (LAI) and gross primary productivity (GPP) in China during 1982–2015 using both satellite observations and reanalysis. Vapor pressure deficit (VPD), evapotranspiration (ET), precipitation (PT), and soil moisture (SM) were used as representative indicators during investigation. The dynamics of LAI and GPP displayed increasing trends on the whole. And significantly changes mostly occurred in eastern and southern China, accompanied by moderate to dense vegetation coverage. An accelerated increasing trend for LAI could be observed since September 2003 (+0.175 per decade), while the growth rate of GPP (+16.76 g C/m2 per decade) basically maintained stable. In terms of hydrometeorological controls, VPD and ET showed consistently increasing trend and SM presented falling trend in the context of warming climate. Through analysis of feature importance index established by Random Forest, the importance of VPD, SM, and PT obviously increased as latitude decreases, which implied the high sensitivity of vegetation in semi-humid\humid regions to water availability. Additionally, divergence between LAI and GPP can be found in southern Tibet Plateau region and coastal regions, which provides compelling evidence that in the case of high LAI, increasing LAI could weaken the improvement effect on GPP. Findings of this study is expected to promote the understanding of long-term vegetation responses to hydrometeorological variations. [ABSTRACT FROM AUTHOR]

• A spatial random forest-based downscaling and merging method (SRF-DM) is proposed. • SRF-DM performs better than the original satellite-based precipitation products. • SRF-DM is more accurate than the classical machine learning-based methods. • Spatial autocorrelation is the most important variable. • SRF-DM has a good performance even when the gauge density is low. Publicly available satellite precipitation products (SPPs) are essential inputs for hydrological models. However, the existing SPPs suffer from coarse resolutions and large uncertainties, which greatly hinder their widespread applications. Thus, to fully utilize the unique characteristics of each individual SPP, a spatial random forest (SRF)-based downscaling and merging (SRF-DM) method is proposed to fuse multiple SPPs and gauge observations in this paper. The proposed method incorporates spatial autocorrelation information between precipitation observations through a covariate. This covariate is estimated using ordinary kriging interpolation on spatial neighbors that have a high correlation with the target location. The proposed method was used to generate 1-km daily precipitation data using the datasets from five satellite precipitation products (IMERG, GSMaP, CHIRPS, MORPH, and PERSIANN) and gauge observations in the period from January 1, 2015 to December 31, 2019 over Sichuan province, China. The performance of SRF-DM was compared with the original SPPs, XGBoost-based downscaling and merging (XGB-DM), two double-layer machine learning (ML) methods, namely RF-RF and RF-artificial neural network (RF-ANN), and three single ML methods, namely RF, ANN, and RF-based Merging Procedure (RF-MEP). The experimental results demonstrate the following findings: (i) SRF-DM is more accurate than the original SPPs across different temporal scales (i.e., daily, monthly, and seasonal) in terms of statistical and categorical accuracy measures, (ii) SRF-DM outperforms the six ML-based methods in estimating high precipitation and capturing spatial precipitation patterns, (iii) the spatial autocorrelation between neighboring precipitation data is the most significant factor in both downscaling and merging, and (iv) the performance of SRF-DM is influenced by gauge density, but it still yields good results even with a limited number of gauges. On the whole, SRF-DM is considered an effective tool for generating daily precipitation products with high accuracy and resolution, which is primarily attributed to its satisfactory performance in regions with diverse precipitation patterns and topography. [ABSTRACT FROM AUTHOR]

Influent quality and quantity were important factors that caused the abnormal operation of WWTP. In this study, the prediction models of influent quality and quantity were established based on four machine learning methods of Linear Regression, Ridge Regression, ElasticNet Regression and Lasso Regression. The meteorological conditions (precipitation and air temperature) and influent indicators (influent quantity, COD, and NH3-N) were used as training data. The influent quantity prediction of the models were evaluated using the historical data obtained from a WWTP located in western China, and the results showed that the normal rates of influent quantity were ranged from 98.9%-100%. The highest accuracy was obtained with Ridge method which was 86.19%. For influent quality (COD) prediction, Ridge method is relatively ideal, with 82% accuracy. For influent quality (NH3-N) prediction, because of higher data normality rates, Lasso and ElasticNet method were more ideal, both with 74% accuracy. Further, in view of the reason of low prediction accuracy, this paper puts forward the idea of model improvement from the three directions of data fluctuation, correlation and amount. It is expected that this study will provide reference for similar research and provide a reference and thought for similar research. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • Thirty-four diffuse fraction and eleven diffuse coefficient models were reviewed and compared. • Nine new diffuse fraction and eight new diffuse coefficient models were developed and validated. • Diffuse fraction and diffuse coefficient general models for climates were established. Abstract In this paper, nine new diffuse fraction and eight new diffuse coefficient models were developed by introducing precipitation as one of the model parameters. Concurrently, thirty-four existing diffuse fraction and eleven existing diffuse coefficient models were reviewed. The existing and proposed models were compared to assess their applicability in different climate zones in China. According to the best models and climate zones, general models were established. Overall, introducing precipitation could effectively improving the underestimation problems of daily diffuse solar radiation; for diffuse fraction models, precipitation had stronger correlation with diffuse radiation than relative humidity in humid areas and weaker correlation in arid areas. For diffuse coefficient models, the models incorporating precipitation could obtain better accuracy than temperature and relative humidity in all climate zones under similar meteorological parameters. Therefore, precipitation should be preferentially adopted, followed by temperature and relative humidity. The general models developed in this paper performed well in estimating the daily diffuse solar radiation. The average coefficient of determination, root mean square error and mean absolute bias error of diffuse fraction general models were 0.82, 1.44 MJm−2 d−1 and 1.04 MJm−2 d−1, respectively; while the corresponding values of diffuse coefficient general models were 0.69, 1.98 MJm−2 d−1 and 1.54 MJm−2 d−1, respectively. The general models could represent the daily diffuse solar radiation estimation of stations without diffuse solar radiation records. [ABSTRACT FROM AUTHOR]

This paper matched the data for all non-financial listed firms in China from 1990 to 2017 with weather data and used temperature and precipitation to represent climate change. Using daily temperature and daily precipitation data, we constructed temperature bins and precipitation bins. This study presents a potential first empirical analysis that applies the risk perception theory to comprehensively examine the impacts of daily temperature and daily precipitation on firms' debt financing costs. Compared to a day with a suitable temperature, a day with temperatures above 30 °C increases firms' debt financing costs by 0.2411%. Compared to a sunny day, a day with torrential rain increases firms' financing costs by 0.2697%. Approximately $122.13 million and $20.49 million in extra costs were incurred across all firms in 2017. We conducted a series of heterogeneity tests. We found that the negative impacts of climate change on debt financing costs are greater for firms that investor considers more vulnerable. Finally, we found two possible mechanisms. First, from the firm's subjective initiative perspective, firms that respond proactively during seasons of high temperatures and heavy rains can reduce the negative impacts. Second, adverse weather caused by climate change may also increase firms' debt financing costs by blocking travel. These results enrich the literature on firms' debt financing costs and contribute to the understanding of climate change's impact on firms. [Display omitted] • An extra high-temperature day increases firms' debt financing cost by 0.2411%. • An extra heavy-rain day increases firms' debt financing costs by 0.2697%. • The negative impacts will be greater for those firms that investor considers more vulnerable. • Firms' response proactively can reduce the negative impacts. • Climate change also may increase firms' debt financing costs by blocking travel. [ABSTRACT FROM AUTHOR]

Short-termintense precipitation (SIP; i.e., convective precipitation exceeding 20mmh21) nowcasting is important for urban flood warning and natural hazards management. This paper presents an algorithm for coupling automatic weather station data and single-polarization S-band radar data with a graph model and a random forest for the nowcasting of SIP. Different from the pixel-by-pixel precipitation nowcasting algorithm, this algorithm takes the convective cells as the basic units to consider their interactions and focuses on multicell convective systems. In particular, the following question could be addressed: Will a multicell convective system cause SIP events in the next hour? First, a method based on spatiotemporal superposition between cells is proposed for multicell systems identification. Then, the graph model is used to represent cell physical attributes and the spatial distribution of the entire system. For each graph model, a fusion operation is used to form a 42-dimensional graph feature vector. Finally, combined with the machine learning approaches, a random forest classifier is trained with the graph feature vector to predict the precipitation. In the experiment, this algorithm achieves a probability of detection (POD) of 79.2% and a critical success index (CSI) of 68.3% with the data between 2015 and 2016 in North China. Compared with other precipitation nowcasting algorithms, the graph model and random forest could predict SIP events more accurately and produce fewer false alarms. [ABSTRACT FROM AUTHOR]

Precipitation prediction is central in hydrology and water resources planning and management. This paper introduces a semi-empirical predictive model to predict monthly precipitation and compares its predictive skill with those of machine learning (ML) methods. The stochastic method presented herein estimates monthly precipitation with one-step-ahead prediction properties. The ML predictive skill of the algorithms is evaluated by predicting monthly precipitation relying on the statistical association between precipitation and environmental and topographic factors. The semi-empirical predictive model features non-negative matrix factorization (NMF) for investigating the influence of multiple predictor variables on precipitation. The semi-empirical predictive model's parameters are optimized with the hybrid genetic algorithm (GA) and Levenberg-Marquardt algorithm (LM), or GALMA, yielding a validated model with high predictive skill. The methodologies are illustrated with data from Hubei Province, China, which comprise 27 meteorological station datasets from 1988–2017. The empirical results provide valuable insights for developing semi-empirical rainfall prediction models. [ABSTRACT FROM AUTHOR]

• Seasonal variations of δ18O and δD in precipitation can be conserved in groundwater. • Amount-weighted isotopic input of rainfall affects seasonal amplitude of groundwater. • The seasonal isotopic strength conserved in groundwater varies with land cover. • Land cover impacts on evaporation loss and hydrodynamic conditions in groundwater. • To land cover, soil has the most important effect on dampening isotopic seasonality. δ18O and δD values in meteoric groundwaters reflect those in their source precipitation, which normally displays seasonal ranges of several to many permil. Normally there are substantial reductions in the groundwater isotopic ranges due to mixing in any vegetation, soil cover and the aquifer itself, plus the distortions that can be introduced by evaporation. Nevertheless, in carbonate karst regions (and others) many spring waters are found to preserve significant proportions of these seasonal signals. They are of current interest because they may also be detected in calcite speleothems precipitated from groundwater in caves, in calcretes and calcareous nodules in soils, etc. thus permitting detailed paleoclimate reconstructions. This paper presents some baseline data on reduction of isotope seasonal signals in groundwater that are pertinent to such speleothem studies. They are from artificial tank experiments at Shawan Karst Test Site, Guizhou, China. The climate is humid subtropical with mean annual precipitation of 1340 mm, >80% falling between May and October. The concrete tanks measure 20 × 5 m, 3 m deep each, point-drained at one end to simulate a spring. They are filled with 2.5 m of local limestone quarry gravel, creating carbonate aquifers with primary porosities ∼ 50%. A first tank is left bare, simulating the rocky desertification found in parts of the region. The other tanks are topped with 0.5 m of local, clay-rich residual soil, one left bare, the other three sown with rainy season corn, permanent grass and permanent shrubs respectively. Measurements cover the three climatic years, November 2015-November 2018. > 90% of the rainfall events were sampled, and the drains every ten days. δ18O and δD in the rainfall displayed strong and regular sinusoidal oscillations with the successive seasons. In the bare rock tank these were reduced (damped) by 56~74% in the different years, with phase (time) lags of 127~134 days at the drain. In the soil-topped tanks damping ranged from 61% to 93% and the time lags from 164 to 202 days: differences between them are not statistically significant but all do differ significantly from the bare rock. In all cases, the responses of the two isotopes were similar in direction and magnitude. The 0.5 m soil cover had the most important effect. The bare rock aquifer conserved the most information. Losses were similar in the four soil-topped tanks, responding to stronger evaporation and obstruction of flow in the soil. These findings are briefly compared to seasonal isotope variations reported in speleothem drip waters in limestone caves in China and elsewhere in the world. [ABSTRACT FROM AUTHOR]

Biological soil crusts are fundamental components and surface landscape in arid regions, and biogeochemical process also depends on them. However, biological soil crusts stoichiometry remains largely unknown in the desert region of Northern China. The influences of precipitation on biological soil crusts stoichiometry is also lacking in desert steppe. This paper aims to investigate the effects of precipitation on the stoichiometry of biological soil crusts in desert steppe. This field study was conducted at Dashuikeng Grassland Research Station, Yanchi County (106°58′E, 37°24′N, average elevation, 1 560 m), Ningxia Hui Autonomous Region, China. This region covers a large ecosystem of desert steppe that characterized by low rainfall and uneven precipitation, along the southwest edge of the Mu Us Sandy Land. In the study desert steppe area on the southeastern margin of the Tengger Desert, the average annual precipitation was only 298.3 mm during the 60 years from 1959 to 2019. The precipitation was regulated by the measurement of artificial rain using rainwater shed and sprinkler irrigation technologies, in order to evaluate precipitation effects on the biological soil crusts in desert steppe compared with natural precipitation. 28 precipitation treatments (6 m×6 m) were set up at the test station in early March 2018, where 3 m wide buffer zone was designed between each time. Control conditions of precipitation were designed via the shelter and sprinkler irrigation system, including 7 precipitation treatments, natural precipitation (CK), drought (1/2 reduction in precipitation, DW1/2 treatment, 1/3 reduction in natural precipitation, DW1/3 treatment, 1/4 reduction in precipitation, DW1/4 treatment), and increasing precipitation (1/2 increase in natural precipitation, IW1/2 treatment, 1/3 increase in natural precipitation, IW1/3 treatment, 1/4 increase in natural precipitation, IW1/4 treatment). Each treatment has 4 spares for the reproductivity. In the same period, small weather stations were set up in the test area. TRIME-PICO TDR Portable Soil Moisture Meter (made in Germany) was used for the data collection of precipitation in the soil water depth of 10 cm. Carbon (C), nitrogen (N) and phosphorus (P) contents of biological soil crusts were measured to examine the C: N: P stoichiometry and its driving factors. Three findings can be achieved: 1) Water-reducing treatment was beneficial to the enrichment of C, N and P in the crust. DW1/4 treatment and natural precipitation treatment posed significant effects on the C content in the crust layer and underlying soil. The water-reducing treatment can reduce the N level of the crust, while increase the N level of the underlying surface. The content of P in the crust layer was the lowest under DW 1/3 treatment, whereas the content of P in the underlying surface reached the peak under DW 1/2 treatment; 2) Natural rainfall and increased precipitation can significantly improve the underlying surface C: N and C: P. There was a reduction of N: P in the crust layer and its underlying surface; 3) Suitable soil moisture conditions promoted the accumulation of SMBC (Soil Microbial Biomass Carbon) and SMBN (Soil Microbial Biomass Nitrogen) on the crust layer and underlying surface, while excessive rainfall caused the loss of soil nutrients, which was not conducive to the accumulation of SMBC and SMBN. The relatively dry soil environment was conducive to the enrichment of soil carbon and nitrogen in the crust layer, provided more nutrients for soil microbial respiration, and was conducive to the accumulation of SMBC and SMBN. In short, the C: N: P stoichiometry of biological soil crusts and underlying surfaces had different responses to precipitation in the desertification areas of northern China. [ABSTRACT FROM AUTHOR]

Water Use Efficiency (WUE) refers to the ratio of water used in plant metabolism over water lost in evapotranspiration, indicating carbon-water coupling in terrestrial ecosystem and its response to global change. It is of great significance to clarify the long-term variation characteristics and influencing factors of WUE for the study of carbon and water cycles and the rational utilization of water resources. This study aims to estimate the vegetation WUE of a research site, particularly on the annual and interannual variation characteristics of WUE, and to establish the response relationship between WUE and key meteorological factors. The data used in this paper mainly include the flux and meteorological information from eight sites in China from January 2003 to December 2010. Unified quality control and interpolation methods were then used to pre-process the flux data. The daily flux and meteorological data were combined into 8-day data sets in each site. Pearson coefficient was used to analyze the correlation between vegetation WUE and Gross Primary Productivity (GPP), ET, average temperature, precipitation, and relative humidity factors. The characteristics and influencing factors of WUE were analyzed and compared in different ecosystem. The results showed that: 1)WUE fluctuated greatly in the whole year without obvious regularity in Qianyanzhou, Xishuangbanna and Dinghushan sites which locate in the tropical monsoon climate region, while WUE showed obvious seasonal variation characteristics in the other five sites, all of which increased significantly from April to July, and reached the maximum during the peak period (July to August), and then WUE decreased gradually from August to October. From November to February of the following year, WUE reached zero. 2) From 2003 to 2010, the annual WUE of alpine meadow (Damxung) was the lowest (0.18 g/kg), whereas, that of tropical rainforest (Xishuangbanna) was the highest (4.20 g/kg). The WUE increased annually in the grassland (Inner Mongolia), broad-leaved evergreen forest (Dinghu Mountain), farmland (Yucheng), alpine meadow (Damxung), and alpine meadow (Haibei), whereas, the decreasing trend occurred in the coniferous forest (Qianyanzhou), tropical rainforest (Xishuangbanna), deciduous broad-leaved and coniferous mixed forests (Changbai Mountain). 3) In the 8-day time scale, there was positively correlation between temperature and WUE in alpine meadow, farmland and grassland ecosystem, whereas there was the negatively correlation between temperature and WUE in forest ecosystem. And the negative correlation between WUE and temperature in coniferous forest ecosystem (R=-0.607, P<0.01) was higher than that in evergreen broad-leaved forest and coniferous broad-leaved mixed forest ecosystem. Compared with forest and farmland ecosystem, WUE in alpine meadow and grassland ecosystem had a higher correlation with relative humidity and precipitation; 4) In different ecosystem, the various approaches were proposed to enhance WUE of terrestrial ecosystem for the sustainable development of agriculture, forestry and animal husbandry. Soil management regulation and canopy pruning can improve water use efficiency in the case of forest ecosystem. In farmland ecosystem, water use efficiency can be elevated from irrigation mode and crop breeding. In alpine meadow and grassland ecosystem, man-made grass, rest grazing and area rotation grazing can be used to enhance grassland carbon sink, further to improve water use efficiency. The findings can provide promising potentials to alleviate water shortage against the background of global warming. [ABSTRACT FROM AUTHOR]

The intensity of interannual variation of spring precipitation over southern China during 1979–2014 and possible reasons for it are investigated in this paper. There is a significant interdecadal change in the intensity of interannual variation of spring precipitation over southern China around 1995/96. The intensity of interannual variation of spring rainfall over South China is stronger during 1979–95 than that during 1996–2014. The possible reason may be the larger amplitude of the sea surface temperature anomaly (SSTA) in the western Pacific Ocean (WP) before 1995/96. The cooler (warmer) SSTA in WP may trigger an abnormal local anticyclone (cyclone) at lower levels. The anomalous southwesterly (northeasterly) flow at the northwestern flank of the WP anticyclone (cyclone) covers South China, transporting more (less) moisture to South China. Meanwhile, the anomalous winds converge (diverge) in South China at lower levels and diverge (converge) at upper levels, which causes the anomalous ascent (descent) to enhance (reduce) the precipitation over there. However, during 1996–2014, the intensity of interannual variation of spring rainfall over the middle and lower reaches of the Yangtze River valley becomes much stronger than that during 1979–95, which is related to the intensified interannual variation of the atmospheric circulation in the middle and high latitudes over Eurasia. The weak (strong) Siberian high and East Asian trough may reduce (enhance) the northerly wind from the middle and high latitudes. As a result, the middle and lower reaches of the Yangtze River valley are subjected to the anomalous southerly wind, favoring more (less) precipitation over there. [ABSTRACT FROM AUTHOR]

Numerous circulation indices have been applied in practical climate services focused on regional precipitation. It is beneficial to identify the most influential or decisive indices, but this is difficult with conventional correlation analyses because of the underlying nonlinear mechanisms for precipitation. This paper demonstrates a set of the most influential indices for July–August precipitation in North China, based on the recursive random forest (RRF) method. These decisive circulation indices include the Polar–Eurasia teleconnection, North African subtropical high ridge position, India–Burma trough, Antarctic Oscillation, Northern Hemisphere polar vortex central latitude, North Atlantic Oscillation, and western Pacific subtropical high northern boundary position. Some of these factors have been recognized as directly influential to the regional precipitation, for example, those of the northwestern Pacific subtropical high; however, some are not easily understood. Decision tree (DT) models using these indices were developed to facilitate composite analyses to explain the RRF results. Taking one of the most interesting DT rules as an example, when the North African subtropical high ridge position is sufficiently far south, an anomalous anticyclone occurs in the upstream and an anomalous cyclone in the downstream of North China. This is unfavorable for northward moisture transport in eastern China and hence causes less precipitation in North China than climatology. The present results are not only helpful for improving diagnostic models of regional precipitation, but also enlightening for exploring how global climate change could impact a region by modulating large-scale circulation patterns. [ABSTRACT FROM AUTHOR]

In this paper, correlation between modern leaf physiognomy and climate in China are examined, to optimize the use of leaf characters as a palaeoclimate proxy. A large dataset was compiled, recording the distribution of leaf physiognomic characters among 3166 native dicot trees species across 732 calibration grids on a county level. Grids span a range of ecological environments (tropical rainforests to alpine shrubs) across humid areas. Thirteen climatic parameters were included for each grid and 22 leaf physiognomic characters were scored for each tree species. The correlation between leaf physiognomic characters and climatic parameters were calculated based on single linear regressions (SLR) and multiple linear regressions (MLR). Results indicate clear spatial distribution patterns, linked to latitude, exist for all leaf characters, with temperature (Coldest Month Mean Temperature, CMMT) and precipitation (Growing Season Precipitation, GSP) being the main climate controls. Moreover, because leaf characters are more closely correlated with Precipitation during the Three Wettest Consecutive Months (P3WET), rather than with Precipitation during the Three Driest Consecutive Months (P3DRY), seasonal variations in rainfall associated with the Asian Monsoon might especially influence leaf physiognomic characters. Closer correlations between leaf physiognomy and climate are seen using MLR compared with SLR; therefore Mean Annual Temperature (MAT) and Mean Annual Precipitation (MAP) based on MLR equations provide the most promising basis for palaeoclimate reconstructions in China. • Spatial distribution of 21 leaf physiognomic characters in China is established. • Relationships between leaf physiognomic characters and climatic parameters are established. • The seasonal variations in rainfall under the Asian monsoon climate shapes leaf physiognomic characters. • Paleoclimate can be reconstructed based on such large-scale modern dataset. [ABSTRACT FROM AUTHOR]

There is an obvious gap between China's current irrigation water use efficiency and the world's advanced level for that of China is only 0.50, whereas that of world's advanced level reaches 0.7-0.8. Therefore, saving water resources, improving agricultural irrigation water efficiency, and ensuring the irrigation efficiency will be a long-term strategic task in the future of China. The purpose of this paper was to make a comprehensive evaluation for water use efficiency of Yellow River irrigation area based on flow-tracing and multiple weights method. In order to make a good comprehensive evaluation for water using efficiency, an open index-evaluation system including 19 deterministic index and 3 sub-systems based on irrigation flow-tracing method. Then, the improved fuzzy evaluation model was constructed based on multiple layer-weight determination method by making use of multi-objective and multi-level system. In the first layer (input layer), the index weight was confirmed by calculating entropy-weight-vector, and in the second layer (criterion layer), the sub-system weight was confirmed by the entropy-weight-vector method, equal-weight method and experience weight method, respectively for multi-layer weights. Finally, obtaining data and comparing different weights from the evaluation results was carried out by the fuzzy evaluation model. The paper took Sanyizhai Irrigation Area in lower reaches of the Yellow River as a case study. Sanyizhai Irrigation Area is located in the eastern plain of Henan Province (34°52N, 144°44E), north of the Yellow River. Sanyizhai Irrigation Area was founded in 1958, and it involves 18 counties scattered in Kaifeng, Shangqiu and Heze in Henan Province, and Shandong Provinces as well. Up to now, the water-diverting capacity of Sanyizhai Irrigation Area is about 150 m3/s, and irrigation area covers 9 counties(districts) mainly in the eastern part of Henan province, that is: Kaifeng, Lankao and Qi counties of Kaifeng City, Minquan County, Ningling County, Suiyang District, Liangyuan District, Sui County and Yucheng County of Shangqiu City respectively. The total land area of Sanyizhai Irrigation Area reaches 4 344.2 km2 and the total cultivated area is 2 701.42 km2. The paper took 10 years data from 2005 to 2014 of Sanyizhai Irrigation Area as a case study to make a comprehensive evaluation for water use efficiency. The 3 kinds of weight was used for the calculation. The results showed that: the first entropy-weight-vector weight was 0.307, 0.348, 0.345, the second equal-weight was 0.333, 0.333, 0.334, and the third subjective weight was 0.400, 0.200, 0.400. The evaluation result presented a general identical trend and showed that weight-influence was very limited for the evaluation result. According to the evaluation result, the irrigation water effect in 2013 was optimal among 10 years with average value 0.993 by comprehensive evaluation. Meanwhile, this evaluation results were in conformity with the actual situation of Sanyizhai Irrigation Area. In 2013 the rainfall and groundwater is relatively small, whereas the practical diverted irrigation intake and irrigation area has kept high level, which has ensured irrigation effect of and crop-harvest. According to the evaluation value of second layer, the dynamic variation process about engineering factors and management factors could be seen clearly, which showed that it kept a low-level development before 2007 and entered into a higher level development after 2009, so it could help us find the existing problems in irrigation process and provides data basis and technical support for the scientific planning and sustainable development of Yellow River Irrigation Area. [ABSTRACT FROM AUTHOR]

Wang, X.; Jiang, R.; Xie, J.; Zhao, Y.; Li, F., and Zhu, J., 2019. Multiscale variability of precipitation and their teleconnection with large-scale climate anomalies: A case study of Xi'an city, China. In: Guido-Aldana, P.A. and Mulahasan, S. (eds.), Advances in Water Resources and Exploration. Journal of Coastal Research, Special Issue No. 93, pp. 417–426. Coconut Creek (Florida), ISSN 0749-0208. The changes of precipitation affect the urban rainstorm waterlogging and water resources management, which have important influences on the sustainable development of society. The primary objective of the paper is to provide theoretical basis for local water resources management and disaster prevention and reduction by analyzing the multiscale variability of precipitation and their teleconnection with large-scale climate anomalies in Xi'an city of China. The change characteristics of precipitation and extreme precipitation indices were investigated using Mann-Kendall test, Spearman's rank correlation and wavelet analysis. The cross wavelet transform and wavelet coherence were used to explore the correlations between the precipitation and large-scale teleconnection indices such as Arctic Oscillation (AO), Southern Oscillation Index (SOI), El Niño-Southern Oscillation (ENSO) and sunspot. Results indicate that: the precipitation was mainly concentrated in summer, followed by autumn. The annual, seasonal and monthly precipitation except June and August had decreasing trends, and the magnitudes of precipitation decrease were more pronounced in spring, April and November. The change patterns varied for extreme precipitation indices. The temporal variation in extreme precipitation indices indicated that the extreme precipitation events had mainly increased during 1971-1990 and 1991-2017, and decreased during 1951-1970 and 1951-2017, especially, increased more pronounced during 1991-2017 and decreased more pronounced during 1951-1970. The precipitation exhibited a fluctuant change from increase to decrease, then increase to decrease and finally increase trends, and there was an abrupt change point in 1965. The annual precipitation had statistically significant cycles of 3-5 year and 7-10 year at 5% significance level. The large-scale teleconnection indices affected the precipitation in Xi'an city. The phase differences between precipitation and teleconnection indices including AO and SOI were in-phase dominated, and anti-phase dominated between precipitation and teleconnection indices including ENSO and sunspot, indicating that the AO and SOI exerted positive climate impacts on the precipitation and vice-versa for ENSO and sunspot. [ABSTRACT FROM AUTHOR]

• The proposed model improves the forecast skill for sub-daily precipitation. • The model was improved by treating precipitation forecasts as censored data. • The proposed model achieves similar forecast skill with an EMOS model. Statistical post-processing methods have been applied in hydrometeorological forecasting to correct the bias and spread error in raw forecasts. Among various post-processing methods, the meta-Gaussian distribution model (MGD) is one of the early successful methods for post-processing of precipitation forecasts and has been applied in the National Weather Service's Hydrologic Ensemble Forecast System (HEFS), together with the mix-type meta-Gaussian distribution model (MMGD). However, recent studies have shown that the original MGD cannot yield reliable forecasts especially for sub-daily precipitation forecasts (e.g., 6-hourly). In this paper, we improved the MGD model by applying the censored maximum likelihood estimation (CMLE) method. We conducted experiments using GEFS reforecasts in Huai river basin in China to evaluate its performance. The results show that the proposed method performs better than the original MGD for sub-daily precipitation forecasts. The proposed method also achieves similar forecast skill with the state-of-the-art censored, shifted Gamma distribution-based ensemble MOS (CSGD-EMOS) if both use ensemble mean as the only predictor. The results indicate that the proposed CMLE-MGD can be useful for further applications such as flood forecasting that needs forecasts of high temporal resolution. [ABSTRACT FROM AUTHOR]

In this paper, we evaluate a 48‐member multiphysics ensemble using the Weather Research and Forecasting (WRF) model for the JJA extreme precipitation and temperature in 2003 over the CORDEX‐EA‐II domain. The simulated precipitation and temperature are reasonable in the subregions controlled by a large‐scale circulation, yet the biases for both precipitation and temperature are evident over the subregions where the effects of mesoscale processes are important. The performance of various combinations of WRF physical schemes for simulating the JJA precipitation is dependent on the region. Meanwhile, the cumulus and microphysical schemes have substantial influences on the simulation of precipitation, and the land surface models and cumulus schemes play crucial roles in the surface temperature. Our analysis shows that the combination of Noah for the land surface process, Lin for the microphysics, G3D for the cumulus parameterization, and CAM for the radiation scheme can provide the most reliable reproduction of both precipitation and temperature extremes over China. Ensemble analysis shows that the simulated climate extremes are usually accompanied by a large ensemble spread, implying sensitivities to the model physical processes in some subregions. The simulated wind fields at low‐to‐middle atmospheric levels display responses to the options of the land surface models and cumulus schemes. The essential impact of the land–atmospheric interaction on simulating the extremes can be largely attributed to the active convective processes. WRF has difficulties in reproducing the observed temporal evolution of the rainfall process, which consists of continuous large rainfall episodes in the observation data set during the simulation period. [ABSTRACT FROM AUTHOR]

The changes of precipitation affect the urban rainstorm waterlogging and water resources management, which have important influences on the sustainable development of society. The primary objective of the paper is to provide theoretical basis for local water resources management and disaster prevention and reduction by analyzing the multiscale variability of precipitation and their teleconnection with large-scale climate anomalies in Xi'an city of China. The change characteristics of precipitation and extreme precipitation indices were investigated using Mann-Kendall test, Spearman's rank correlation and wavelet analysis. The cross wavelet transform and wavelet coherence were used to explore the correlations between the precipitation and large-scale teleconnection indices such as Arctic Oscillation (AO), Southern Oscillation Index (SOI), El Niño- Southern Oscillation (ENSO) and sunspot. Results indicate that: the precipitation was mainly concentrated in summer, followed by autumn. The annual, seasonal and monthly precipitation except June and August had decreasing trends, and the magnitudes of precipitation decrease were more pronounced in spring, April and November. The change patterns varied for extreme precipitation indices. The temporal variation in extreme precipitation indices indicated that the extreme precipitation events had mainly increased during 1971-1990 and 1991-2017, and decreased during 1951-1970 and 1951-2017, especially, increased more pronounced during 1991-2017 and decreased more pronounced during 1951-1970. The precipitation exhibited a fluctuant change from increase to decrease, then increase to decrease and finally increase trends, and there was an abrupt change point in 1965. The annual precipitation had statistically significant cycles of 3-5 year and 7-10 year at 5% significance level. The large-scale teleconnection indices affected the precipitation in Xi'an city. The phase differences between precipitation and teleconnection indices including AO and SOI were in-phase dominated, and anti-phase dominated between precipitation and teleconnection indices including ENSO and sunspot, indicating that the AO and SOI exerted positive climate impacts on the precipitation and vice-versa for ENSO and sunspot. [ABSTRACT FROM AUTHOR]

This paper investigates the annual and seasonal precipitation variability at 20 stations over upper Indus River basin (UIRB) by using an innovative trend analysis (ITA) method, Mann-Kendall (MK) and Sen's slope estimator tests. Moreover, the annual precipitation time series is investigated for five precipitation intensities i.e. light, low, moderate, high and heavy; and on a seasonal scale for extreme values i.e. light and heavy precipitation intensities. Annual precipitation showed an increasing trend at four stations in the northeast region and a decreasing trend at two stations in the southeast region of UIRB. On a seasonal scale, the results showed that Garidopatta, Peshawar, Astore and Gupis stations are more sensitive to seasonal precipitation variabilities. Changes in heavy precipitation (> 90th percentile) are found more severe in winter and summer seasons in the northern regions suggesting a probable flooding aggravation. However, a reduction in flooding event observed during the spring season in the southern regions of the UIRB indicating a shift in the precipitation regime from south to north. The results of light precipitation (< 10th percentile) intensity showed that winter and spring seasons are more vulnerable to the occurrence of prolonged drought events. Moreover, ITA method has the advantage of detecting the sub-trends in the precipitation series because of its ability to present the results in graphical format. The results of this study could help to understand seasonal and annual precipitation variability over UIRB and will have imperative insinuations for further studies. [ABSTRACT FROM AUTHOR]

It is important that developing and utilizing of unconventional water resources such as pond water in the lowland area of North China Plain due to the shortage of fresh water resource. Thus, making clear the storage capacity of pond as irrigation water resources in years with different amount of precipitation and studying the relationship between precipitation and pond water storage are of great significance to the usages of multiple water resources in agriculture. Taking Nanpi County in Hebei Province as study area, this paper used the methods of Cl- and stable isotope balance to calculate the contribution of precipitation to pond water in rainy season. Besides, based on the Landsat TM remote sensing data during 2013- 2015, the improved normalized difference water body index was used to extract the pond information and analyze the water storage capacity of ponds in Nanpi County in years with different amount of precipitation. The results showed that precipitation is the main source of pond water, and the groundwater was the second source. According to the calculation of Cl- and stable isotope, precipitation accounts for 74.3%-86.0% of water supply for medium and small ponds during the rainy season in wet year, however, it accounts for only less than 30% of water supply for large ponds. The ponds are mainly small ponds with area less than 0.5 hm2. The number and the water storage area of ponds with different areas changed with precipitation. The water storage capacity of ponds in flat water year, dry year and wet year were 1.49×107, 0.84×107 and 1.90× 107 m3, respectively. The results provide important infromation for the pond water management and utilization in the lowland area of North China Plain. [ABSTRACT FROM AUTHOR]

Deriving high quality precipitation estimates at high spatial resolution is of prime importance for many hydrological, meteorological, and environmental investigations. Rain gauge observations and satellite-derived precipitation data are two main sources of precipitation estimates. Gauge observations are accurate and reliable, but are heavily point-based and sparse in areas of rugged or complex terrains. Satellite-derived precipitation products can cover large areas, but they are generally characterized by inherent bias. To optimize the use of both datasets, we propose in this paper, a downscaling-integration framework to generate high quality monthly precipitation datasets at 1 km spatial resolution by merging rain gauge observations and TRMM 3B43 products. Firstly, an area-to-point kriging (ATPK) approach is used to downscale the original TRMM product to 1 km, so as to ensure a fair comparison with rain gauge data. Then, the downscaled TRMM precipitation datasets are integrated with the gauge observations using geographically weighted regression kriging (GWRK). The geographical factors (i.e. longitude, latitude and elevation) are also used as auxiliary variables in the GWRK model. Applying this approach to an experiment conducted at the middle and lower reaches of the Yangtze River in China from 2001 to 2014 shows that: (1) the downscaled monthly TRMM precipitation data by ATPK are more accurate than the original TRMM estimates; (2) the GWRK model employing the downscaled TRMM precipitation data and geographical factors provides better monthly precipitation estimates than the conventional ordinary kriging (OK) interpolation and the commonly used merging methods (i.e. geographical difference analysis, GDA and kriging with external drift, KED); (3) the GWRK method reduces the influence of the inaccuracy (bias) of satellite-derived precipitation data on the precipitation estimates compared to GDA. The approach presented in this study has provided an efficient alternative for solving the scale mismatch problem between point-based gauge data and low resolution satellite data, and producing improved precipitation data at high spatial resolution. [ABSTRACT FROM AUTHOR]

A source-oriented Community Multiscale Air Quality (CMAQ) model driven by the meteorological fields generated by the Weather Research and Forecasting (WRF) model was used to study the dry and wet deposition of nitrate (NO 3 − ), sulfate (SO 4 2 − ), and ammonium (NH 4 + ) ions in the Jiuzhaigou National Nature Reserve (JNNR), China from June to August 2010 and to identify the contributions of different emission sectors and source regions that were responsible for the deposition fluxes. The model performance is evaluated in this paper and the source contribution analyses are presented in a companion paper. The results show that WRF is capable of reproducing the observed precipitation rates with a Mean Normalized Gross Error (MNGE) of 8.1%. Predicted wet deposition fluxes of SO 4 2 − and NO 3 − at the Long Lake (LL) site (3100 m a.s.l.) during the three-month episode are 2.75 and 0.34 kg S(N) ha − 1 , which agree well with the observed wet deposition fluxes of 2.42 and 0.39 kg S(N) ha − 1 , respectively. Temporal variations in the weekly deposition fluxes at LL are also well predicted. Wet deposition flux of NH 4 + at LL is over-predicted by approximately a factor of 3 (1.60 kg N ha − 1 vs . 0.56 kg N ha − 1 ), likely due to missing alkaline earth cations such as Ca 2 + in the current CMAQ simulations. Predicted wet deposition fluxes are also in general agreement with observations at four Acid Deposition Monitoring Network in East Asia (EANET) sites in western China. Predicted dry deposition fluxes of SO 4 2 − (including gas deposition of SO 2 ) and NO 3 − (including gas deposition of HNO 3 ) are 0.12 and 0.12 kg S(N) ha − 1 at LL and 0.07 and 0.08 kg S(N) ha − 1 at Jiuzhaigou Bureau (JB) in JNNR, respectively, which are much lower than the corresponding wet deposition fluxes. Dry deposition flux of NH 4 + (including gas deposition of NH 3 ) is 0.21 kg N ha − 1 at LL, and is also much lower than the predicted wet deposition flux. For both dry and wet deposition fluxes, predictions from the 12-km resolution nested domain are similar to those from the 36-km resolution parent domain. [ABSTRACT FROM AUTHOR]

Loess Plateau has great uncertainty on drought occurrence due to climate change. This paper analyzes the evolution of precipitation, potential evapotranspiration and standardized precipitation evapotranspiration index (SPEI) based on the Coupled Model Inter-comparison Project Phase 5 (CMIP5) data and regional downscaling model (RegCM4.0). Results indicate that, under RCP2.6 Scenario, the precipitation will increase significantly (5% confidence level) at the rate of 16.40 mm/10 a. However, the potential evapotranspiration is showing non-significant decreasing trend at the rate of 2.16 mm/10 a. Moreover, the SPEI will decrease in the south and northernmost area and increase in the central northern area of Loess Plateau. Under RCP8.5 Scenario, the precipitation will increase significantly (5% confidence level) at the rate of 19.12 mm/10 a. The potential evapotranspiration will non-significantly decrease at the rate of 2.16 mm/10 a and the SPEI is showing increasing trend almost in the whole Loess Plateau. Generally, Loess Plateau is becoming wetter in the central part under RCP2.6 Scenario and the wet area will be enlarged to almost the whole plateau under RCP8.5 Scenario. Based on the results, the water resources will increase under global warming, which may alleviate the water scarcity issue in the Loess Plateau. [ABSTRACT FROM AUTHOR]

Background and aims: Precipitation and nitrogen (N) deposition are predicted to increase in northern China. The present paper aimed to better understand how different dominant species in semi-arid grasslands in this region vary in their litter decomposition and nutrient release responses to increases in precipitation and N deposition. Methods: Above-ground litter of three dominant species (two grasses, Agropyron cristatum and Stipa krylovii, and one forb, Artemisia frigida) was collected from areas without experimental treatments in a semi-arid grassland in Inner Mongolia. Litter decomposition was studied over three years to determine the effects of water and N addition on litter decomposition rate and nutrient dynamics. Results: Litter mass loss and nutrient release were faster for the forb species than for the two grasses during decomposition. Both water and N addition increased litter mass loss of the grass A. cristatum, while the treatments showed no impacts on that of the forb A. frigida. Supplemental N had time-dependent, positive effects on litter mass loss of the grass S. krylovii. During the three-year decomposition study, the release of N from litter was inhibited by N addition for the three species, and it was promoted by water addition for the two grasses. Across all treatments, N and potassium (K) were released from the litter of all three species, whereas calcium (Ca) was accumulated. Phosphorus (P) and magnesium (Mg) were released from the forb litter but accumulated in the grass litter after three years of decomposition. Conclusions: Our findings revealed that the litter decomposition response to water and N supplementation differed among dominant plant species in a semi-arid grassland, indicating that changes in dominant plant species induced by projected increases in precipitation and N deposition are likely to affect litter decomposition, nutrient cycling, and further biogeochemical cycles in this grassland. The asynchronous nutrient release of different species' litter found in the present study highlights the complexity of nutrient replenishment from litter decomposition in the temperate steppe under scenarios of enhancing precipitation and N deposition. [ABSTRACT FROM AUTHOR]

Summary: In this paper, the trends of the annual streamflow and precipitation and cross correlations between them were analyzed in nine large river basins of China during 1956–2005. The results indicate that: (1) the annual mean streamflow decreases in arid and semi-arid regions of north China; however, increasing trends occur in south and Southwest China; (2) the annual streamflow and precipitation exhibit reasonable correlation in nine large river basins except those located in inland areas. The annual streamflow over most areas of China is fed by precipitation; however, the decline in streamflow is faster than the decreases of precipitation since 1970s in the arid and semi-arid regions of north China. The relationship between the annual precipitation and streamflow presents a non-stationary state in north China. This non-stationary relationship is strongly influenced by both human activities and precipitation changes; (3) a significant increase of water use might be the major factor responsible for the steeper decline in streamflow than in precipitation in Haihe River, Yellow River and Songliao River basins in north China. In inland river areas, increase of water use and actual evapotranspiration might result in decline in streamflow although precipitation has an increase tendency. This paper sheds light on the non-stationary relationship between annual precipitation and streamflow and possible underlying causes, which will be helpful for a better understanding of the changes of precipitation and streamflow in China at large scale and in other regions of the world. [Copyright &y& Elsevier]

Middle and eastern routes of the South-North Water Diversion Project (SNWDP) of China, which are approximately located within the area 28°-42°N and 110°-122°E, are being constructed. This paper investigates the past climatic variations on various time scales using instrumental and proxy data. It is found that annual mean surface air temperature has increased significantly during the past 50-100 years, and winter and spring temperatures in the northern part of the region have undergone the most significant changes. A much more significant increase occurs for annual mean minimum temperature and extreme low temperature than for annual mean maximum temperature and extreme high temperature. No significant trend in annual precipitation is found for the region as a whole for the last 50 and 100 years, although obvious decadal and spatial variation is detectable. A seesaw pattern of annual and summer precipitation variability between the north and the south of the region is evident. Over the last 100 years, the Haihe River basin has witnessed a significant negative trend of annual precipitation, but no similar trend is detected for the Yangtze and Huaihe River basins. Pan evaporation has significantly decreased since the mid-1960s in the region in spite of the fact that the trend appears to have ended in the early 1990s. The negative trend of pan evaporation is very significant in the plain area between the Yangtze and Yellow Rivers. There was a notable series of dry intervals lasting decades in the north of the region. The northern drought of the past 30 years is not the most severe in view of the past 500 years; however, the southern drought during the period from the 1960s to the 1980s may have been unprecedented. The dryness-wetness index (DWI) shows significant oscillations with periodicities of 9.5 and 20 years in the south and 10.5 and 25 years in the north. Longer periodicities in the DWI series include 160-170- and 70-80-yr oscillations in the north, and 100-150-yr oscillations in the south. The observed climate change could have implications for the construction and management of the SNWDP. The official approval and start of the hydro project was catalyzed by the severe multiyear drought of 1997-2003 in the north, and the operation and management of the project in the future will also be influenced by climate change-in particular by precipitation variability. This paper provides a preliminary discussion of the potential implications of observed climate change for the SNWDP. [ABSTRACT FROM AUTHOR]

The start and duration of a drought are often not easy to define. The advance and onset are usually slow and the social and economic impacts can then extend over a long period. In this study, the Standardized Precipitation Index (SPI) was used to describe and compare droughts in different periods and regions under different climatic conditions. The importance of the index lies in its simplicity and in its ability to identify the beginning and end of a drought event. Thus it may be a useful tool for drought contingency planning. This paper examines the SPI by evaluating its application using historical precipitation data from nine weather stations in China. Different time series data covering the period 1980 to 2011 and for differing timescales of 3, 9 and 24 months were used. The results underline the potential of the SPI to inform drought alerts and forecasting efforts as part of a drought contingency planning strategy. [ABSTRACT FROM AUTHOR]

Wang, X.; Jiang, R.; Xie, J.; Zhao, Y.; Li, F., and Zhu, J., 2019. Multiscale variability of precipitation and their teleconnection with large-scale climate anomalies: A case study of Xi'an city, China. In: Guido-Aldana, P.A. and Mulahasan, S. (eds.), Advances in Water Resources and Exploration. Journal of Coastal Research, Special Issue No. 93, pp. 417–426. Coconut Creek (Florida), ISSN 0749-0208. The changes of precipitation affect the urban rainstorm waterlogging and water resources management, which have important influences on the sustainable development of society. The primary objective of the paper is to provide theoretical basis for local water resources management and disaster prevention and reduction by analyzing the multiscale variability of precipitation and their teleconnection with large-scale climate anomalies in Xi'an city of China. The change characteristics of precipitation and extreme precipitation indices were investigated using Mann-Kendall test, Spearman's rank correlation and wavelet analysis. The cross wavelet transform and wavelet coherence were used to explore the correlations between the precipitation and large-scale teleconnection indices such as Arctic Oscillation (AO), Southern Oscillation Index (SOI), El Niño-Southern Oscillation (ENSO) and sunspot. Results indicate that: the precipitation was mainly concentrated in summer, followed by autumn. The annual, seasonal and monthly precipitation except June and August had decreasing trends, and the magnitudes of precipitation decrease were more pronounced in spring, April and November. The change patterns varied for extreme precipitation indices. The temporal variation in extreme precipitation indices indicated that the extreme precipitation events had mainly increased during 1971-1990 and 1991-2017, and decreased during 1951-1970 and 1951-2017, especially, increased more pronounced during 1991-2017 and decreased more pronounced during 1951-1970. The precipitation exhibited a fluctuant change from increase to decrease, then increase to decrease and finally increase trends, and there was an abrupt change point in 1965. The annual precipitation had statistically significant cycles of 3-5 year and 7-10 year at 5% significance level. The large-scale teleconnection indices affected the precipitation in Xi'an city. The phase differences between precipitation and teleconnection indices including AO and SOI were in-phase dominated, and anti-phase dominated between precipitation and teleconnection indices including ENSO and sunspot, indicating that the AO and SOI exerted positive climate impacts on the precipitation and vice-versa for ENSO and sunspot. [ABSTRACT FROM AUTHOR]

This paper analyzed the data of temperature and precipitation in Turpan and Nantong, in the west and east of China, respectively, during the period of 1961 –2007 by linear groWh rate, moving average methods, Mann-Kendall nonparametric and moving Ttests. The result indicated that the annual average temperature in Turpan had a linear groWh rate of 0.42 °C/10 a, while the annual average temperature in Nantong had a linear tendency of 0.48 °C/10 a. The annual average maximum temperature in Turpan with linear groWh rate of 0.16 °C/10 a was lower than 0.56 °C/ 10 a of Nantong, and the annual average minimum temperature in Turpan with linear growh rate of 0.86 °C/10 a was higher than 0.70 °C/10 a of Nantong. The annual average temperature of Turpan increasing was 7 years earlier than Nantong. The annual average maximum temperature of Turpan increasing was 5 years later than Nantong. The annual average minimum temperature of Turpan increased as same as Nantong in 1986, but the curve showed different process completely. The abrupt change year of average temperature between Turpan and Nantong were same in 1996, and the abrupt change year of average maximum temperature of Turpan was 5 years later than Nantong. For precipitation, Turpan and Nantong had the same trend of increased and decreased. The reason of almost identical trend of temperature and precipitation between Turpan and Nantong may be urbanization and population increasing influencing climate change in a small area. [ABSTRACT FROM AUTHOR]

Since climatic condition is the important foundation for human subsistence and development and the key factor in sustainable development of economy and society, climate change has been a global issue attracting great attentions of politicians, scientists, governments, and the public alike throughout the world. Existing climate regionalization in China aims to characterize the regional differences in climate based on years of the mean value of different climate indexes. However, with the accelerating climate change nowadays, existing climate regionalization cannot represent the regional difference of climate change, nor can it reflect the disasters and environmental risks incurred from climate changes. This paper utilizes the tendency value and fluctuation value of temperature and precipitation from 1961 to 2010 to identify the climate change quantitatively, and completes the climate change regionalization in China (1961-2010) with county administrative regionalization as the unit in combination with China's terrain feature. Level-I regionalization divides China's climate change (1961-2010) into five tendency zones based on the tendency of temperature and precipitation, which are respectively Northeast China-North China warm-dry trend zone, East China-Central China wet-warm trend zone, Southwest China-South China dry-warm trend zone, Southeast Tibet-Southwest China wet-warm trend zone, and Northwest China-Qinghai-Tibet Plateau warm-wet trend zone; level-II regionalization refers to fourteen fluctuation regions based on level-I regionalization according to the fluctuation of temperature and precipitation. [ABSTRACT FROM AUTHOR]

Summary The climate elasticity of runoff is an important indicator that is used to quantify the relationship between changes in runoff and changes in climate variables. It is a function of both climate and catchment characteristics. Recently, Yang and Yang (2011) proposed an analytical derivation of climate elasticity (YY2011), in which a parameter n was used to represent the impact of the catchment characteristics. In China, both climate and catchment characteristics have large spatial variations. To understand the spatial variation of hydrologic response to climate change, this paper divided China into 210 catchments, further calculated the parameter n , and then estimated the climate elasticity and evaluated the contribution of climate change to runoff for each catchment. The results show that n ranges from 0.4 to 3.8 (with a mean of 1.3 and a standard deviation of 0.6), which has a logarithmic relationship with catchment slope; the precipitation elasticity ranges from 1.1 to 4.8 (with a mean of 1.9 and a standard deviation of 0.6), which shows a large regional variation, smaller values (1.1–2.0) mainly appearing in Southern China, the Songhua River basin and the Northwest, and larger values (2.1–4.8) mainly appearing in the Hai River basin, the Liao River basin and the Yellow River basin. In addition, climate contribution to runoff exhibits a large regional variation, the largest positive values (1.1–3.1%/a) occurring in the Northwest, the largest negative values (−1.0 to −0.5%/a) occurring in the Hai River basin and the middle reach of the Yellow River basin. In theory, the YY2011 method is a first-order approximation. The approximation underestimates the precipitation ( P ) contribution to runoff when P increases and overestimates that when P decreases, and the relative error has a median of ∼3% and a maximum of ∼20% when 10% precipitations change in those catchments of China. [ABSTRACT FROM AUTHOR]

Scarcity or unavailability of precipitation observation creates difficulties in hydrologic modeling of mountainous sections of the arid region of northwest China (34°-50°N, 72°-107°E). Tropical Rainfall Measuring Mission (TRMM) precipitation products may be a potential substitute, but they should be evaluated and corrected with ground observation data before application. In this paper, two TRMM Multisatellite Precipitation Analysis (TMPA) precipitation products were evaluated by gauge observations, using indices such as frequency bias index, probability of detection, false alarm ratio, relative mean bias, Nash-Sutcliffe efficiency, and correlation coefficient. Terrain variables were extracted from a digital elevation model, and their rotated principal components were determined to establish a stepwise regression model to adjust TMPA precipitation. Additionally, a back-propagation (BP) neural network was established to correct TMPA precipitation. The results showed that TMPA had an unsatisfactory detection ability in the study area for both precipitation occurrence and amount. TMPA precipitation corrected by a stepwise regression method showed some improvement, but only the results for TRMM 3B43 on a subregion scale were acceptable. The BP neural network method showed better results than the stepwise regression method, and both TRMM 3B42 and TRMM 3B43 corrected by the former method on a subregion scale could be acceptable. Both methods were spatial-scale dependent and showed better results on a subregion scale than on a larger scale. [ABSTRACT FROM AUTHOR]

Highlights: [•] This paper examines the impacts of rain gauge densities on runoff simulations. [•] Variation of rain gauge densities impacts the areal mean rainfall considerably. [•] The locations of rain gauges impact the model performances considerably. [•] Great impacts on model performances occurring when rain gauges fall below 0.4 station per 1000km2. [Copyright &y& Elsevier]

This paper explored the projected changes in precipitation, maximum temperature (T max), and minimum temperature (T min) over the Yangtze River Basin (YRB) based on 23 Global climate models (GCMs) from the Coupled Model Intercomparison Project phase 6 (CMIP6). The Empirical Quantile Mapping (EQM) approach was firstly applied to correct the biases in the GCMs. Next, the future changes were investigated by analyzing the multi-model ensemble (MME) of the bias-corrected dataset during 2025–2044 (near-term), 2045–2064 (mid-term), and 2081–2100 (long-term) periods, with reference to the baseline period 1995–2014, under three integrated scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5) of the Shared Socioeconomic Pathways (SSPs) and the Representative Concentration Pathways (RCPs). Results show that: (1) the biases in CMIP6 GCMs can be effectively corrected by EQM, and the MME performs better than individual models for each climatic variable. (2) Precipitation over the YRB from 2025 to 2100 is projected to increase at the rate of 9.66 mm/decade, 13.45 mm/decade, and 21.01 mm/decade under SSP1‐2.6, SSP2‐4.5, and SSP5‐8.5, respectively. In the long-term, the annual precipitation is projected to increase by 10.41%, 10.66% and 15.80%, under SSP1‐2.6, SSP2‐4.5 and SSP5‐8.5, respectively. (3) T max (T min) over the YRB is projected to increase by 0.09 (0.07) °C/decade, 0.29 (0.27) °C/decade, and 0.66 (0.64) °C/decade under SSP1‐2.6, SSP2‐4.5, and SSP5‐8.5, respectively. In the long-term, T max (T min) averaged over the YRB is projected to increase by 1.75 (1.50) °C, 2.72 (2.54) °C and 5.04 (4.85) °C under SSP1‐2.6, SSP2‐4.5 and SSP5‐8.5, respectively. (4) Uncertainties in the projected precipitation and temperature over the YRB were reduced based on MME. But further researches, such as selecting the superior models with respect to the regional climate of the YRB from the CMIP6 and using the ensemble methods that assign weight based on the performance of each model, are still needed to provide more reliable climate projections. • Projected changes were explored by 23 CMIP6 GCMs under the three SSP-RCP scenarios. • Biases in the GCMs were corrected by Empirical Quantile Mapping (EQM) method. • Multi-model ensemble (MME) was used to reduce the uncertainties. [ABSTRACT FROM AUTHOR]

Temporal and spatial evolution characteristics of the 30-60 day oscillation (intraseasonal oscillation, ISO) of summer rainfall in China and the effects of East Asian monsoon on the rainfall ISO are analyzed in this paper. Results show that the annual and decadal variations of the oscillation exist between 1960 and 2008, and the intensity is weakest in the late 1970s and early 1980s. In the typical strong years of the rainfall ISO obtained from empirical orthogonal functions (EOF mode 1), an anticyclone is in northwestern Pacific and a cyclone is in the east of China. In the typical weak years, the wind ISO is much weaker. The low-frequency zonal wind and water vapor transport from the low latitudes to mid-latitudes in the typical strong years, and the oscillation strength of diabatic heating is much stronger than that in the weak years of the rainfall ISO. The anomaly characteristics of the rainfall ISO show anti-phases between the Yangtze River basin and south of China. As for the typical strong years of the rainfall ISO in the Yangtze River basin (EOF mode 2), the main oscillation center of water vapor is in the east of China (20-30°N, 110-130°E). In the peak (break) phase of the rainfall oscillation, a low-frequency cyclone (anticyclone) is in the Yangtze River basin and an anticyclone (cyclone) is near Taiwan Island. In addition, the peak rainfall corresponds to the heat source in the Yangtze River basin and the heat sink in the Qinghai-Tibet Plateau. As for the typical strong years of the rainfall ISO in the south of China, the main oscillation center of water vapor is south of 20°N. In the peak (break) phase of the rainfall ISO, a low-frequency cyclone (anticyclone) is in the south of China and an anticyclone (cyclone) is in the Philippines. The peak rainfall corresponds to the heat source in the south of China and the South China Sea, and the heat sink in the west of Indochina. [ABSTRACT FROM AUTHOR]

Abstract: The availability of precipitation data with high spatial resolution is of fundamental importance in several applications such as hydrology, meteorology and ecology. At present, there are mainly two sources of precipitation estimates: raingauge stations and remote sensing technology. However, a large number of studies demonstrated that traditional point measurements based on raingauge stations cannot reflect the spatial variation of precipitation effectively, especially in ungauged basins. The technology of remote sensing has greatly improved the quality of precipitation observations and produced reasonably high resolution gridded precipitation fields. These products, derived from satellites, have been widely used in various parts of the world. However, when applied to local basins and regions, the spatial resolution of these products is too coarse. In this paper, we present a statistical downscaling algorithm based on the relationships between precipitation and other environmental factors in the Qaidam Basin such as topography and vegetation, which was developed for downscaling the spatial precipitation fields of these remote sensing products. This algorithm is demonstrated with the Tropical Rainfall Measuring Mission (TRMM) 3B43 dataset, the Digital Elevation Model (DEM) from the Shuttle Radar Topography Mission (SRTM) and SPOT VEGETATION. The statistical relationship among precipitation, DEM and Normalized Difference Vegetation Index (NDVI), which is a proxy for vegetation, is variable at different scales; therefore, a multiple linear regression model was established under four different scales (0.25°, 0.50°, 0.75° and 1.00°, respectively). By applying a downscaling methodology, TRMM 3B43 0.25°×0.25° precipitation fields were downscaled to 1×1km pixel precipitation for each year from 1999 to 2009. On the basis of three criteria, these four downscaled results were compared with each other and the regression model established at the resolution of 0.50° was selected as the final downscaling algorithm in this study. The final downscaled results were validated by applying the observations for a duration of 11years obtained from six raingauge stations in the Qaidam Basin. These results indicated that the downscaled result effectively captured the trends in inter-annual variability and the magnitude of annual precipitation with the coefficient of determination r 2 ranging from 0.72 to 0.96 at six different raingauge stations. [Copyright &y& Elsevier]