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A method named BTREC, which is in fact an extension of tracking a radar echo by cross-correlation (TREC) method, is proposed based on the Barnes filter in this paper. BTREC is an efficient objective analysis method for smoothing the motion vectors of radar echo patterns. A comparative analysis of the BTREC vectors and the COTREC (another extension of TREC, which is restricted by the 2D continuity equation) vectors is conducted. The results show that the BTREC method corrects the noise and inconsistencies in TREC vectors (often induced by shielding, ground clutter, and rapid morphological changes of the radar patterns) more thoroughly than COTREC. Then the BTREC, COTREC, and TREC methods are applied to extrapolate the real radar echo pattern over Jiangxi Province in China for two cases (1900–2100 UTC 4 June and 0900–1100 UTC 30 June 2018). Results show that the BTREC method performs the best out of the three methods to forecast the radar echo patterns in the following 1 and 2 h, and has the least distortion. To further confirm that, 892 radar composite reflectivity mosaic images with precipitation in summer (June–August) over Jiangxi are collected to test the three methods. The results show that in the 1- and 2-h extrapolations, the mean differences of the threat score (TS), correlation (CORR), and probability of detection (POD) between BTREC and TREC are obviously more than 0, while that of the false alarm ratio (FAR) is remarkably less than 0 (the threshold to identify whether a grid is correctly predicted is set to 10 dBZ). Although the mean difference of TS, CORR, POD, and FAR between COTREC and TREC have similar variation, their magnitudes are obviously decreased (especially for the 2-h extrapolation). This further indicates that the BTREC method obviously improves the forecast of the radar echo pattern within the next 2 h compared to the COTREC and TREC methods. [ABSTRACT FROM AUTHOR]

Numerical weather prediction (NWP), when accessible, is a crucial input to short-term solar power forecasting. WRF-Solar, the first NWP model specifically designed for solar energy applications, has shown promising predictive capability. Nevertheless, few attempts have been made to investigate its performance under high aerosol loading, which attenuates incoming radiation significantly. The North China Plain is a polluted region due to industrialization, which constitutes a proper testbed for such investigation. In this paper, aerosol direct radiative effect (DRE) on three surface shortwave radiation components (i.e., global, beam, and diffuse) during five heavy pollution episodes is studied within the WRF-Solar framework. Results show that WRF-Solar overestimates instantaneous beam radiation up to 795.3 W m−2 when the aerosol DRE is not considered. Although such overestimation can be partially offset by an underestimation of the diffuse radiation of about 194.5 W m−2, the overestimation of the global radiation still reaches 160.2 W m−2. This undesirable bias can be reduced when WRF-Solar is powered by Copernicus Atmosphere Monitoring Service (CAMS) aerosol forecasts, which then translates to accuracy improvements in photovoltaic (PV) power forecasts. This work also compares the forecast performance of the CAMS-powered WRF-Solar with that of the European Centre for Medium-Range Weather Forecasts model. Under high aerosol loading conditions, the irradiance forecast accuracy generated by WRF-Solar increased by 53.2% and the PV power forecast accuracy increased by 6.8%. Significance Statement: Numerical weather prediction (NWP) is the "go-to" approach for achieving high-performance day-ahead solar power forecasting. Integrating time-varying aerosol forecasts into NWP models effectively captures aerosol direct radiation effects, thereby enhancing the accuracy of solar irradiance forecasts in heavily polluted regions. This work not only quantifies the aerosol effects on global, beam, and diffuse irradiance but also reveals the physical mechanisms of irradiance-to-power conversion by constructing a model chain. Using the North China Plain as a testbed, the performance of WRF-Solar on solar power forecasting on five severe pollution days is analyzed. This version of WRF-Solar can outperform the European Centre for Medium-Range Weather Forecasts model, confirming the need for generating high spatial–temporal NWP. [ABSTRACT FROM AUTHOR]

While previous work on the climatology of Northern China has focused on mean wind speed, wind gusts have received comparatively less attention but are equally important to various users. In this paper, an observed hourly maximum gust wind speeds (HMGS) dataset across North China has been created by using time series from 174 meteorological stations. The dataset offers superior quality, high spatiotemporal near-surface HMGS series for North China spanning from 2015 to 2022. The objective of this study is first to improve our understanding of the spatiotemporal gusts climatology in North China by analyzing the observed gust data. Second, we aim to supplement the observational data by using gust analysis and forecast data with a high spatial–temporal resolution from model simulations. The spatial characteristics of the seasonal cycle of the simulated analysis of mean HMGS and the performance in predicting gusts based on the geographical locations and elevations of the validation stations were investigated by comparing it with the observations. Results indicate the following: 1) Wind direction and intensity are affected by the terrain and climate conditions of different weather stations. Stations situated along the Bohai Bay coastal region and at higher-elevation areas of North China exhibit a higher mean HMGS than those located in the coastal and inland plains. 2) The probability density function curves for wind speed and wind direction exhibit notable variations across different elevation intervals. The contribution of moderate and strong gust wind speeds increases gradually with increasing altitude, while the gust directions in mountainous areas exhibit relatively consistent patterns due to the increased exposure to synoptic-scale forcing at higher elevations. 3) The nowcasting prediction system analysis of mean HMGS provides a higher horizontal resolution that is capable of capturing the contrasts between land and sea, as well as the influence of high HMGS associated with large-scale circulations in high-elevation regions. Significance Statement: The purpose of this study is to better understand the spatiotemporal gust climatology in North China and the performance of the model-simulated gust analysis and forecast data. This is important because gusts conditions differ due to varying topographic and climatic conditions of different weather stations. Our results provide a valuable insight into the climatological variations of HMGS, their drivers, and identify the deficiencies in the model-simulation gusts. [ABSTRACT FROM AUTHOR]

Drought is a common natural disaster that greatly affects the crop yield and water supply in China. However, the spatiotemporal characteristics of drought in China are not well understood. This paper explores the spatial and temporal distributions of droughts in China over the past 40 years using multiscale standardized precipitation evapotranspiration index (SPEI) values calculated by monthly precipitation and temperature data from 612 meteorological stations in China from 1980 to 2019 and combines the space–time cube (STC), Mann–Kendall test, emerging spatiotemporal hot-spot analysis, spatiotemporal clustering, and local outliers for the analysis. The results were as follows: 1) the drought frequency and STC show that there is a significant difference in the spatiotemporal distribution of drought in China, with the most severe drought in Northwest China, followed by the western part of Southwest China and the northern part of North China. 2) The emerging spatiotemporal hot-spot analysis of SPEI6 over the past 40 years reveals two cold spots in subregion 4, indicating that future droughts in the region will be more severe. 3) A local outlier analysis of the multiscale SPEI yields a low–low outlier in western North China, indicating relatively more severe year-round drought in this area than in other areas. The low–high outlier in central China indicates that this region was not dry in the past and that drought will become more severe in this region in the future. [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]

It is well known that El Niño–Southern Oscillation (ENSO) can influence the East Asian winter climate by modifying the atmospheric circulation over the western North Pacific (WNP). While the impact on precipitation in southeastern China has been extensively studied, the ENSO signal in surface air temperature (SAT) remains overlooked. In this paper, we identify robust ENSO footprints in the winter daily minimum SAT in southeastern China, with El Niño winters generally accompanied by warmer-than-normal minimum SAT anomalies. In contrast, the responses of maximum SAT are weak and negligible, suggesting a diurnal cycle–dependent ENSO influence. Further analysis indicates that this diurnal cycle dependence stems primarily from the disparate surface radiative heating between day and night induced by ENSO-related local total cloud cover (TCC) change. The warmer minimum SAT occurring in the early morning of El Niño winters is mainly caused by the enhanced surface downward longwave radiative heating as a result of the TCC increase. However, in the afternoon of El Niño winters, although the anomalous horizontal advection of warm air plays a role, there is surface radiative cooling as the weakening of solar radiation due to TCC reflection overwhelms the increase in downward longwave radiation, which leads to a weakened sensible heat flux and thus has a cooling effect on the SAT. Ultimately, these two processes effectively cancel each other out and together produce insignificant maximum SAT responses. Our conclusions carry important implications for the seasonal to interannual winter climate prediction in southeastern China. Significance Statement The purpose of this study is to better understand how ENSO influences winter surface air temperature in southeastern China. Despite insignificant responses of winter daily maximum temperature to ENSO, we find here a robust ENSO influence on the daily minimum temperature, with warm anomalies tending to occur during El Niño events. Further evidence suggests that this diurnal cycle–dependent ENSO influence is mainly caused by ENSO-induced change in local cloudiness, which has a different radiative heating effect between day and night. Our findings are of great importance for improving regional climate prediction, which will benefit social and economic activities. [ABSTRACT FROM AUTHOR]

Prediction of summer precipitation in north China (NCP) has long been a challenge partly because its low correlation with previous sea surface temperature (SST) anomalies (SSTA) limits the application of SST in NCP prediction. This study aims to extract optimal predictors of NCP from the SST field using an objective method—empirically optimal screening (EOS). It finds that the optimal precursory signal of NCP lies in the change of SSTA from winter to spring rather than the SSTA itself. This study identifies two optimal precursory signs predicting a positive (negative) NCP anomaly: the anomalous SST cooling (warming) from winter to spring in the coastal area of Somalia and Peru. Interestingly, these two presummer conditions have considerable independence, but they lead to a similar summer development of La Niña (El Niño). In summer, the tropical precipitation anomaly pattern associated with La Niña (El Niño) development excites a meridional wave train over the western Pacific and the circumglobal teleconnection in the Northern Hemisphere. Both of the anomalous wave trains show abnormal high (low) pressure over northeast Asia, which induces the south (north) wind anomalies over north China and produces abundant (deficient) precipitation there. These results highlight the importance of the SST evolution from winter to spring, break through the limitation of SST application in NCP prediction, and thus bring a prospect of improving NCP forecast skills. Significance Statement: Sea surface temperature (SST) anomalies are most used as predictors in climate prediction. However, the forecast of summer precipitation in north China is limited by its low correlation with prior SST anomalies. In this paper, we find that the optimal precursory signal of north China precipitation (NCP) is not the SST anomaly itself, but the changes of SST anomalies from winter to spring in the coastal area of Somalia and Peru. These two precursory signals are almost independent yet indicate similar summer situations leading to NCP anomaly. These results highlight the importance of the dynamic evolution of sea surface temperature in improving the forecast skill of NCP. [ABSTRACT FROM AUTHOR]

Model calibration has always been one major challenge in the hydrological community. Flood scaling properties (FS) are often used to estimate the flood quantiles for data-scarce catchments based on the statistical relationship between flood peak and contributing areas. This paper investigates the potential of applying FS and multivariate flood scaling properties [multiple linear regression (MLR)] as constraints in model calibration. Based on the assumption that the scaling property of flood exists in four study catchments in northern China, eight calibration scenarios are designed with adopting different combinations of traditional indicators and FS or MLR as objective functions. The performance of the proposed method is verified by employing a distributed hydrological model, namely, the Soil and Water Assessment Tool (SWAT) model. The results indicate that reasonable performance could be obtained in FS with fewer requirements of observed streamflow data, exhibiting better simulation of flood peaks than the Nash-Sutcliffe efficiency coefficient calibration scenario. The observed streamflow data or regional flood information are required in the MLR calibration scenario to identify the dominant catchment descriptors, and MLR achieves better performance on catchment interior points, especially for the events with uneven distribution of rainfall. On account of the improved performance on hydrographs and flood frequency curve at the watershed outlet, adopting the statistical indicators and flood scaling property simultaneously as model constraints is suggested. The proposed methodology enhances the physical connection of flood peak among sub-basins and considers watershed actual conditions and climatic characteristics for each flood event, facilitating a new calibration approach for both gauged catchments and data-scarce catchments. [ABSTRACT FROM AUTHOR]

The spatial distribution of summer rainfall anomalies over eastern China often shows a tripole pattern with rainfall anomalies over the Yangtze River basin varies in opposite phase with North China and South China. It is not clear whether this tripole pattern is an intrinsic atmospheric mode or it is remotely forced. Using two sets of model outputs from 20 models participating in phase 5 of the Coupled Model Intercomparison Project (CMIP5), this paper investigates the driving mechanisms of this leading rainfall mode and its major influencing factors. One set (piControl) is fully coupled atmosphere–ocean simulations under constant preindustrial forcing and the other (sstClim) is atmosphere-alone models forced by prescribed climatological sea surface temperatures (SSTs). By comparing results from these two different sets of simulations, it is found that the tripole pattern is the leading mode of summer precipitation variability over eastern China with or without oceanic forcing. It can be regarded as an intrinsic atmospheric mode although air–sea interaction can modify its temporal variability. The cyclonic–anticyclonic atmospheric circulation anomaly over the northern North Pacific is identified as a key factor in both experiments. As atmospheric internal variability, it is related to a circumglobal zonal wave train propagating along the westerly jet stream. When air–sea interactions involved, modulation from SST anomalies is exerted through the meridional Pacific–Japan/East Asia–Pacific wave train propagating along the East Asian coast. Our results suggest that the North Pacific could be another key region providing potential predictability to the East Asian monsoon in addition to the Indo-Pacific. [ABSTRACT FROM AUTHOR]

The southwest vortex (SWV) is a critical weather system in China, but our knowledge of this system remains incomplete. Here, we investigate the cloud properties in the SWV. First, we search for the SWVs with time steps and center locations that are consistent between the SWV yearbook and ERA-Interim reanalysis data. Second, we supplement these SWVs' life spans and movement paths. Third, we relocate the Fengyun (FY) satellite FY-4A cloud retrievals in the 10° × 10° region centered on each SWV and analyze the cloud occurrence frequency (COF), cloud-top height (CTH), and cloud optical thickness (COT). A distribution mode of cloud types is summarized from the COFs, with water clouds, supercooled clouds, mixed clouds, ice clouds, cirrus clouds, and overlap clouds occurring sequentially from west to east. The CTH probability density (PD) distribution features a significant north–south difference. In addition, the COT PD distributions exhibit a common trend: with increasing COT, the PD increases rapidly and then slowly before peaking, whereupon the PD decreases abruptly. From spring to summer, the region with the highest convective COF shifts from the northeast to the northwest, and an east–west gradient of the convective COF appears in autumn and winter. Furthermore, we investigate the cloud properties during SWV-related heavy rainfall. Heavy rain occurs mainly in the west of the SWV, and convective clouds are mainly in the northwest, partly in the southwest and near the SWV center. The average CTH in heavy rainfall is generally higher than 6 km, and the average COT is greater than 20. Significance Statement: The southwest vortex (SWV) is an important weather system in China. However, we do not yet comprehensively know this weather system. The cloud properties can indicate the structures of weather systems and are key parameters in numerical weather prediction (NWP) models. Thus, investigating cloud properties is necessary and meaningful to understand the SWV and accurately predict SWV-related precipitation in NWP models. In this paper, a typical distribution mode of six cloud types in the SWV is summarized from the cloud occurrence frequency, and the distribution features of convective clouds, cloud-top height, and cloud optical thickness in the SWV are analyzed. Furthermore, the cloud properties in SWV-related heavy rain are also studied. [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]

Drought forecasts could effectively reduce the risk of drought. Data-driven models are suitable forecast tools because of theirminimal information requirements. Themotivation for this study is that because most data-driven models, such as autoregressive integrated moving average (ARIMA) models, can capture linear relationships but cannot capture nonlinear relationships they are insufficient for long-term prediction. The hybrid ARIMA-support vector regression (SVR) model proposed in this paper is based on the advantages of a linear model and a nonlinear model. The multiscale standard precipitation indices (SPI: SPI1, SPI3, SPI6, and SPI12) were forecast and compared using the ARIMA model and the hybrid ARIMA-SVR model. The performance of all models was compared using measures of persistence, such as the coefficient of determination, root-mean-square error, mean absolute error, Nash-Sutcliffe coefficient, and kriging interpolation method in the ArcGIS software. The results show that the prediction accuracies of the multiscale SPI of the combined ARIMA-SVR model and the single ARIMA model were related to the time scale of the index, and they gradually increase with an increase in time scale. The predicted value decreases with increase in lead time. Comparing the measured data with the predicted data from the model shows that the combined ARIMA-SVR model had higher prediction accuracy than the single ARIMA model and that the predicted results 1-2 months ahead show reasonably good agreement with the actual data. [ABSTRACT FROM AUTHOR]

Changing pathways of soil moisture loss, either directly from soil (evaporation) or indirectly through vegetation (transpiration), are an indicator of ecosystem and land hydrological cycle responses to the changing climate. Based on the ratio of transpiration to evaporation, this paper investigates soil moisture loss pathway changes across China using five reanalysis-type datasets for the past and Coupled Model Intercomparison Project Phase 6 (CMIP6) climate projections for the future. The results show that across China, the ratio of vegetation transpiration to soil evaporation has generally increased across vegetated land areas, except in grasslands and croplands in north China. During 1981–2014, there was an increase by 51.4 percentage points (pps, p < 0.01) on average according to the reanalyses and by 42.7 pps according to 13 CMIP6 models. The CMIP6 projections suggest that the holistic increasing trend will continue into the twenty-first century at a rate of 40.8 pps for SSP585, 30.6 pps for SSP245, and −1.0 pps for SSP126 shared socioeconomic pathway scenarios for the period 2015–2100 relative to 1981–2014. Major contributions come from the increases in vegetation transpiration over the semiarid and subhumid grasslands, croplands, and forestlands under the influence of increasing temperatures and prolonged growing seasons (with twin peaks in May and October). The future increasing vegetation transpiration ratio in soil moisture loss implies the potential of regional greening across China under global warming and the risks of intensifying land surface dryness and altering the coupling between soil moisture and climate in regions with water-limited ecosystems. [ABSTRACT FROM AUTHOR]

Obtaining high-quality quantitative precipitation forecasts is a key precondition for hydrological forecast systems. Due to multisource uncertainties (e.g., initial conditions, model structures, and parameters), raw forecasts are subject to systematic biases; hence, statistical postprocessing is often required to reduce these errors before the forecasts can proceed to hydrological applications. Machine learning (ML) algorithms are canonical statistical models, and they are diverse in type and variation. It is important to verify and compare their performance in the same scenario (e.g., precipitation postprocessing). In this paper, we conduct a large-scale comparison study for the major ML models with diverse model structures and regularization strategies as postprocessors for improving the quality of precipitation forecasts. Specifically, we compare the efficiency and effectiveness of 21 ML algorithms on solving this task. Daily reforecast precipitation with lead times up to 8 days from the Global Ensemble Forecast System and corresponding observations are employed to determine the usability of different models in the Yalong River basin in China. The performance of each model is validated by a group of carefully designed experiments and statistical metrics. The results reveal that improvements in model structures are more effective than regularization strategies. Among these algorithms, the optimized extra-trees regressor exhibits the best performance, effectively reducing overestimation and achieving the best skill in forecasting precipitation. Eleven ensemble members and a 3-day forward-rolling time window can be used as predictors to obtain the best model performance. The systematic experiments and findings also offer useful guidelines for other related studies. [ABSTRACT FROM AUTHOR]

Cascade reservoirs were designed for the Yalong River basin, which ranks as the third-largest hydropower base in China. Reservoir impoundment has certain impacts on local climate, but few researches focus on this field. This paper integrates spatiotemporal analysis techniques and geostatistical methods to identify the reservoir projected meteorological indices (MIs) variations as well as the impact scope under different circumstances. Results show that the reservoir projected variations of moisture indexes were much more significant than temperatures. Anthropogenic disturbance has led to a dramatic decrease of relative humidity in the past decade, far beyond its periodic amplitude range. Moreover, dry valleys faced more serious drought risk, but reservoir impoundment of this region alleviated the local drought risk. The daily minimum temperature, relative to the maximum temperature, was more sensitive to catchment changes with an earlier appearance of temperature zone variation. Since impounding, the MIs' internal relationships in nondry valleys varied significantly more than that in dry valleys, with a positive correlation of 0.7 between the relative humidity and precipitation weakening greatly. The severe warming hotspots are distributed in the upper reach and have a probability of 0.95-1 to exceed the 1.5°C IPCC control target. This study provides references for the disturbance of reservoir impoundment to local climate at multiple temporal--spatial scales under different circumstances and contributes to the MIs variation pattern identification and quantitative risk assessment. [ABSTRACT FROM AUTHOR]

The Dual-Frequency Precipitation Radar (DPR) on board the Global Precipitation Measurement (GPM) mission core satellite provides the new-generation global observation of rain since 2014. The main objective of this paper is to evaluate the suitability and limitation of GPM-DPR level-2 products over China. The DPR rain rate products are compared with rain gauge data during the summers of 5 years (2014-18). The ground observation network is composed of more than 50 000 rain gauges. The DPR precipitation products for all scans (DPR_NS, DPR_MS, and DPR_HS) generally underestimate rain rates. However, DPR_MS agrees better with gauge estimates thanDPR_NS and DPR_HS, yielding the lowest mean error, systematic deviation, and highest Pearson correlation coefficient. In addition, all three swath types show obvious overestimation over gauge estimates between 0.5 and 1mmh-1 and underestimation when gauge estimates are larger than 1mmh-1. The DPR_HS and DPR_MS agree better with gauge estimates below and above 2.5mmh-1, respectively. A deeper investigation was carried out to analyze the variation of DPR_MS's performance with respect to terrains over China. An obvious underestimation, relative to gauge estimates, occurs in Tibetan Plateau while a slight overestimation occurs in the North China Plain. Furthermore, our comprehensive analysis suggests that in Sichuan Basin, the DPR_MS exhibit the best agreement with gauge estimates. [ABSTRACT FROM AUTHOR]

Variability in soil moisture has implications for regional terrestrial environments under a warming climate. This paper focuses on the spatiotemporal variability in the intra-annual persistence of soil moisture in China using the fifthgeneration reanalysis dataset by the European Centre for Medium-Range Weather Forecasts for the period 1979-2018. The results show that in China, the mean intra-annual persistence in the humid to arid zones increased from 60 to 115 days in the lower layer but decreased from 19 to 13 days and from 25 to 14 days in the upper and root layers, respectively. However, these changes were strongly attenuated in extremely dry and wet regions due to the scarcity of soil moisture anomalies. Large changes in persistence occurred in the lower soil layer in dryland areas, with a mean difference of up to 40 days between the 2010s and the 1980s. Overall increasing trends dominated the large-scale spatial features, despite regional decreases in the eastern arid zone and the North and Northeast China plains. In the root layer, the two plains experienced an expanded decrease while on the Tibetan Plateau it was dominated by decadal variability. These contrasting changes between the lower and root layers along the periphery of the transition zone was a reflection of the enhanced soil hydrological cycle in the root layer. The enhanced persistence in drylands lower layer is an indication of the intensified impacts of soil moisture anomalies (e.g., droughts) on terrestrial water cycle. These findings may help the understanding of climate change impacts on terrestrial environments. [ABSTRACT FROM AUTHOR]

The harmonic constants extracted from tidal gauge stations and satellite altimeter observations are usually sparsely distributed in the continental marginal seas, but they are precious data for addressing the main characteristics of different constituents. In this paper, a dynamically constrained interpolation methodology (DCIM) is developed and applied to interpolate the observed harmonic constants of the M2 constituent from satellite altimeter observations in the Bohai, Yellow, and East China Seas (BYECS) with those from tidal gauge stations for validation. In the DCIM, the tide model provides dynamical constraints to interpolate the observations, and the adjoint assimilation method provides iterative optimization for the interpolated results by adjusting key model parameters. In particular, a substantial quantity of enhanced "observations" generated from the interpolated results of the domain are further interpolated with the sparse observations in the subdomain. The final interpolated results for each subdomain, naturally blending the dynamical constraints from the dynamical model with statistical information from observations, can describe the main characteristics of the M2 constituent in the BYECS, the continental shelf sea of the East China Sea, and the Zhejiang-Fujian coastal area, respectively. The results indicate that the DCIM is feasible and effective to utilize the observations to obtain high-accuracy cotidal charts for regional ocean. [ABSTRACT FROM AUTHOR]

In aiming for better access to climate change information and for providing climate service, it is important to obtain reliable high-resolution temperature simulations. Systematic comparisons are still deficient between statistical and dynamic downscaling techniques because of their inherent unavoidable uncertainties. In this paper, 20 global climate models (GCMs) and one regional climate model [Providing Regional Climates to Impact Studies (PRECIS)] are employed to evaluate their capabilities in reproducing average trends of mean temperature (Tm), maximum temperature (Tmax), minimum temperature (Tmin), diurnal temperature range (DTR), and extreme events represented by frost days (FD) and heat-wave days (HD) across China. It is shown generally that bias of temperatures from GCMs relative to observations is over ±1°C across more than one-half of mainland China. PRECIS demonstrates better representation of temperatures (except for HD) relative to GCMs. There is relatively better performance in Huanghuai, Jianghuai, Jianghan, south Yangzi River, and South China, whereas estimation is not as good in Xinjiang, the eastern part of northwest China, and the Tibetan Plateau. Bias-correction spatial disaggregation is used to downscale GCMs outputs, and bias correction is applied for PRECIS outputs, which demonstrate better improvement to a bias within ±0.2°C for Tm, Tmax, Tmin, and DTR and ±2 days for FD and HD. Furthermore, such improvement is also verified by the evidence of increased spatial correlation coefficient and symmetrical uncertainty, decreased root-mean-square error, and lower standard deviation for reproductions. It is seen from comprehensive ranking metrics that different downscaled models show the most improvement across different climatic regions, implying that optional ensembles of models should be adopted to provide sufficient high-quality climate information. [ABSTRACT FROM AUTHOR]

Climate classification aims to divide a given region into distinct groups on the basis of meteorological variables, and it has important applications in fields such as agriculture and buildings. In this paper, we propose a novel spectral clustering–based method to partition 661 meteorological stations in China into several zones. First, the correlations are analyzed among five meteorological variables: daily average temperature, average relative humidity, sunshine hours, diurnal temperature range, and atmospheric pressure. The resulting weak linear correlation supports the classification under multiple views. Next, a similarity matrix/graph is constructed by combining the advantages of k-nearest-neighbor and sparse subspace representation. The blocking effect of the similarity matrix confirms the rationality of the classification. Then, we consider respectively the climate classification under a single view and multiple views. For the single view, atmospheric pressure has the highest imbalance degree, and sunshine hours and diurnal temperature range have the strongest classification consistency. The consistency rates are improved evidently in a multiple-view situation. Afterward, we propose a determining method for the number of clusters and make a sensitivity analysis on various parameters. Finally, we provide a further statistical analysis on the classification results and compare the consistency with another climate dataset. All experimental results show that the proposed classification method is feasible and effective in the climate zoning of China. [ABSTRACT FROM AUTHOR]

Any skillful prediction is of great benefit to North China, a region that is densely populated and greatly impacted by droughts. This paper reports potential predictability of North China summer drought 1 month ahead based on hindcasts for 1961–2005 from the "ENSEMBLES" project. Correlation scores of the standardized precipitation–evapotranspiration index and standardized precipitation index reach 0.49 and 0.39, respectively. The lower-level northwestern Pacific cyclonic circulation anomaly (NWPCCA) and East Asian upper-tropospheric temperature (UTT) cooling are the crucial circulations with regard to summer drought. Two sources of predictability are identified: 1) Pacific–Japan and Silk Road teleconnections forced by well-established eastern Pacific Ocean El Niño sea surface temperature anomalies (SSTA) in summer, when the two key circulations are both well predicted because of a good prediction of enhanced equatorial central Pacific (CP) rainfall and Indian rainfall deficit, and 2) the subtropical atmosphere–ocean coupling associated with CP El Niño developing, when the skill mainly arises from the reasonable prediction of NWPCCA. In observations, the NWPCCA persists from the preceding spring to summer through a wind–evaporation–SST feedback related to the Pacific meridional mode (PMM). In predictions, the persistence of the NWPCCA is mainly forced by the enhanced convection over the subtropical central North Pacific due to the persistence of the PMM-related meridional SSTA gradient over the CP. This predicted SSTA suppresses the equatorial Pacific rainfall, contributing to low prediction skill for the East Asian UTT cooling. This study demonstrates the importance of extratropical signals from the preceding season in North China summer drought prediction. [ABSTRACT FROM AUTHOR]

In this paper, we examined the spatial and temporal variations in precipitation amount, frequency, and intensity in China based on daily precipitation data from 2050 weather stations from 1973 to 2016. We used two Markov chain parameters to quantify the wet persistence and dry persistence that characterizes the temporal pattern of wet and dry days, respectively. We found that China's annual precipitation changed little from 1973 to 2016, but varied dramatically from 524 to 688mm yr-1, with an average of 592 mm yr-1, during this period. China's precipitation frequency, the number of days with effective precipitation (.0.1mmday21) in a year, significantly decreased at a rate of 0.9 days decade21 from 1973 to 2016, but precipitation intensity significantly increased at a rate of 0.12 mm day-1 decade-1 during the same period. Of the changes in China's total precipitation amount, precipitation intensity played a dominant role, contributing 70.8%, while precipitation frequency contributed the remaining 29.2%. Little change was found in the wet persistence in China over the period of 1973-2016, but the dry persistence significantly increased with an average increasing trend of 1.62 3 1023 probability per decade during the same period, and no significant correlations were found between these two variables. China's precipitation also changed nonuniformly in space, with increasing trends in precipitation amount, frequency, intensity, and wet persistence in western and northeastern China but decreasing trends in the Sichuan basin, northeast of Inner Mongolia, and the Beijing-Tianjin-Hebei region. [ABSTRACT FROM AUTHOR]

In karst terrains, fractures and conduits often occur in clusters, forming groundwater quick flow belts, which are the major passages of groundwater and solute transport. We propose a cost-effective method that utilizes precipitation and spring discharge data to identify groundwater quick flow belts by the multitaper method (MTM). In this paper, hydrological processes were regarded as the transformation of precipitation signals to spring discharge signals in a karst spring catchment. During the processes, karst aquifers played the role of signal filters. Only those signals with high energy could penetrate through aquifers and reflect in the spring discharge, while other weak signals were filtered out or altered by aquifers. Hence, MTM was applied to detect and reconstruct the signals that penetrate through aquifers. Subsequently, by analyzing the reconstructed signals of precipitation with those of spring discharge, we acquired the hydraulic response time and identified the quick flow belts. Finally, the methods were applied to the Niangziguan Spring (NS) catchment, China. Results showed that the hydraulic response time of the spring discharge to precipitation was 3 months at Pingding County; 4 months at Yuxian County, Yangquan City, Xiyang County, and Heshun County; and 27 months at Shouyang County and Zouquan County. These results suggested that Pingding County is located at a groundwater quick flow belt, which is a major passage of groundwater and contaminants, in the NS catchment. This is important since Pingding County is not only the key development area of karst groundwater but also the key conservation area for sustainable development of karst groundwater resources in NS catchment. [ABSTRACT FROM AUTHOR]

The systematic bias of the estimated average temperature using daily Tmax and Tmin records relative to the standard average temperature of four time-equidistant observations and its effect on the estimated trend of long-term temperature change have not been well understood. This paper attempts to evaluate the systematic bias across mainland China using the daily data of national observational stations. The results revealed that the positive bias of annual mean temperature was large, reaching 0.58°C nationally on average; regional average bias was lowest in the northwest arid region and highest in the Qinghai–Tibetan Plateau; the bias was low in spring and summer and high in autumn and winter, reaching its lowest point in mid- and late May and highest point in early November. Furthermore, the bias showed a significant upward trend in the past 50 years, with a rising rate of 0.021°C (10 yr)−1, accounting for about 12% of the overall warming as estimated from the data of the observational network; the largest positive trend bias was found in the northwest arid region, while the east monsoon region experienced the smallest change; the most remarkable increase of the bias occurred after early 1990s. These results indicate that the customarily applied method to calculate daily and monthly mean temperature using Tmax and Tmin significantly overestimates the climatological mean and the long-term trend of surface air temperature in mainland China. [ABSTRACT FROM AUTHOR]

This paper presents a study on long-term surface solar radiation (SSR) changes over China under clear- and all-sky conditions and analyzes the causes of the "dimming" and "brightening." To eliminate the nonclimatic signals in the historical records, the daily SSR dataset was first homogenized using quantile-matching (QM) adjustment. The results reveal rapid dimming before 2000 not only under all-sky conditions, but also under clear-sky conditions, at a decline rate of −9.7 ± 0.4 W m−2 decade−1 (1958–99). This is slightly stronger than that under all-sky conditions at −7.4 ± 0.4 W m−2 decade−1, since the clear-sky dimming stopped 15 years later. A rapid "wettening" of about 40-Pa surface water vapor pressure (SWVP) from 1985 to 2000 was found over China. It contributed 2.2% to the SSR decline under clear-sky conditions during the whole dimming period (1958–99). Therefore, water vapor cannot be the main cause of the long-term dimming in China. After a stable decade (1999–2008), an intensive brightening appeared under the clear-sky conditions at a rate of 10.6 ± 2.0 W m−2 decade−1, whereas a much weaker brightening (−0.8 ± 3.1 W m−2 decade−1) has been observed under all-sky conditions between 2008 and 2016. The remarkable divergence between clear- and all-sky trends in recent decades indicates that the clouds played two opposite roles in the SSR changes during the past 30 years, by compensating for the declining SSR under the cloud-free conditions in 1985–99 and by counteracting the increasing SSR under cloud-free conditions in 2008–16. Aerosols remain as the main cause of dimming and brightening over China in the last 60 years, although the clouds counteract the effects of aerosols after 2000. [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]

This paper presents a method to homogenize China's surface solar radiation (SSR) data and uses the resulting homogenized SSR data to assess the SSR trend over the period 1958-2016. Neighboring surface sunshine duration (SSD) data are used as reference data to assess the SSR data homogeneity. A principal component analysis is applied to build a reference series, which is proven to be less sensitive to occasional data issues than using the arithmetic mean of data from adjacent stations. A relative or absolute test is applied to detect changepoints, depending on whether or not a suitable reference series is available. A quantile-matching method is used to adjust the data to diminish the inhomogeneities. As a result, 60 out of the 119 SSR stations were found to have inhomogeneity issues. These were mainly caused by changes in instrument and observation schedule. The nonclimatic changes exaggerated the SSR change rates in 1991-93 and resulted in a sudden rise in the national average SSR series, causing an unrealistically drastic trend reversal in the 1990s. This was diminished by the data homogenization. The homogenized data show that the national average SSR has been declining significantly over the period 1958-90; this dimming trend mostly diminished over the period 1991-2005 and was replaced by a brightening trend in the recent decade. From the homogenized SSR data, the 1958-90 and 1958-2005 dimming rate is estimated to be -6.13 ± 0.47 and -5.08 ± 0.27W m-2 decade-1, respectively, and the 2005-16 brightening rate is 6.13 ± 1.77W m-2 decade-1. [ABSTRACT FROM AUTHOR]

An EF4 supercellular tornado hit Funing County, Yancheng, Jiangsu Province, China, from about 1410 to 1500 local standard time 23 June 2016, causing 98 fatalities and 846 injuries. It was the deadliest tornado in the past 40 years in China. This paper documents the storm environment, evolution of the radar signatures, realtime operational tornado warning services, and the damage distribution during this event. The tornado was spawned from a supercell that developed ahead of an upper-level trough extending southwestward from a low pressure vortex in northeast China and dissipated following the occlusion of the tornado vortex. The radarbased rotational velocity of the mesocyclone peaked at 42.2ms21. The strength of the tornado vortex signature (gate-to-gate azimuthal radial velocity difference) peaked at 84.5ms21. Surface observations at 1-min intervals from a mesoscale network of in situ surface weather stations revealed the surface wind pattern associated with the mesocyclone, such as convergent and rotational flows. The tornado formed after the peak updraft strength of the supercell, producing a damage swath that was 34.5 km long and with a maximum width of 4.1 km. The review of the tornado warning process for this event reveals that there is much work to be done to develop operational tornado forecast and warning services for China. [ABSTRACT FROM AUTHOR]

This paper describes the study of the relationship between the thermal configuration of the Bay of Bengal (BOB)-Tibetan Plateau (TP) region and the precipitation anomaly in Yunnan, a province in China, in May using ERA-Interim data and precipitation data for May from 125 meteorological stations across Yunnan for 1979-2014. Results from the analysis indicate that the interannual variability of May precipitation in Yunnan is significantly modulated by the BOB-TP thermal configuration. Model runs with a linear baroclinic model suggest physical consistency. The thermal conditions over the BOB mainly impact the May precipitation anomaly in Yunnan via changes in water vapor transport from the eastern BOB northeastward to southwestern Yunnan. The second factor influencing precipitation anomalies relates to the characteristics and variability of cold air transport from the TP to northeastern Yunnan. When the BOB (the TP) is occupied by positive (negative) diabatic heating, a thermal gradient with a warmer (colder) center over the BOB and a colder (warmer) center over the TP is established, and more-than-normal (less than normal) precipitation in Yunnan will occur in May. This relationship can persist from April to the following May to some extent; therefore, the BOB-TP thermal configuration in April could be used to forecast May precipitation in Yunnan. [ABSTRACT FROM AUTHOR]

This paper analyzes the relationship between the 1000-850-hPa layer perturbation potential energy (LPPE) as the difference in local potential energy between the actual state and the reference state and the East Asian summer monsoon (EASM) using reanalysis and observational datasets. The EASM is closely related to the first-order moment term of LPPE (LPPE1) from the preceding March to the boreal summer over three key regions: the eastern Indian Ocean, the subtropical central Pacific, and midlatitude East Asia. The LPPE1 pattern (−, +, +), with negative values over the eastern Indian Ocean, positive values over the subtropical central Pacific, and positive values over East Asia, corresponds to negative LPPE1 anomalies over the south of the EASM region but positive LPPE1 anomalies over the north of the EASM region, which lead to an anomalous downward branch over the southern region but an upward branch over the northern region. The anomalous vertical motion affects the local meridional circulation over East Asia that leads to a southwesterly wind anomaly over East Asia (south of 30°N) at 850 hPa and anomalous downward motion over 100°-120°E (along 25°-35°N), resulting in a stronger EASM, more kinetic energy over the EASM region, and less boreal summer rainfall in the middle and lower reaches of the Yangtze River valley (24°-36°N, 90°-125°E). These LPPE1 anomalies in the eastern Indian Ocean and subtropical central Pacific appear to be connected to changes in local sea surface temperature through the release of latent heat. [ABSTRACT FROM AUTHOR]

This paper combines observations, climatic analysis, and numerical modeling to investigate the Tibetan Plateau's (TP) surface heating conditions' influence on extreme persistent precipitation events (PEPEs) in southeastern China. Observations indicated an increase of TP surface air temperature 3-4 days prior to extreme persistent precipitation events in southeastern China. NCEP reanalysis data revealed a significant low pressure anomaly in southern China and a high pressure anomaly in northern China during extreme persistent precipitation event periods. Using correlation analysis and random resampling nonparametric statistics, a typical PEPE event from 17 to 25 June 2010 was selected for numerical simulation. The Weather Research and Forecasting (WRF) Model was used to investigate the impact of the TP's surface heating on the evolution of this event. Three contrasting WRF experiments were conducted with different surface heating strengths by changing initial soil moisture over the TP. Different soil conditions generate different intensities of surface sensible heat fluxes and boundary layer structures over the TP resulting in two main effects on downstream convective rainfall: modulating large-scale atmospheric circulations and modifying the water vapor transport at southern China. Increased surface heating in the TP strengthens a high pressure system over the Yangtze Plain, thereby blocking the northward movement of precipitation. It also enhances the water vapor transport from the South China Sea to southern China. The combined effects substantially increase precipitation over most of the southeastern China region. [ABSTRACT FROM AUTHOR]

In coastal regions, compound flooding, driven by multiple flood hazard sources, can cause greater damage than when the flood drivers occur in isolation. This study focuses on compound flooding from extreme precipitation and storm surge in China's Qiantang Estuary. We quantify the potential of compound flooding by measuring bivariate joint statistical dependence and joint return period (JRP). We find a significant positive dependence between the two flood drivers considered, as indicated by Kendall's rank correlation coefficients. Compound events occur frequently, with an average of 2.65 events per year from 1979 to 2018, highlighting the significant concern of compound flooding for this estuary. Using a copula model, we demonstrate that considering the dependence between the two flood drivers shortens the JRP of compound flooding compared to the JRP assuming total independence. For a 1-in-10-yr precipitation event and 1-in-10-yr storm surge event, the JRP is 1 in 100 years when assuming total independence. However, it decreases to 1 in 32.44 years when considering their dependence. Ignoring the dependence between flood drivers can lead to an increase in the JRP of compound events, resulting in an underestimation of the overall flood risk. Our analysis reveals a strong link between the weather patterns creating compound events and extreme storm surge only events with tropical cyclone activity. Additionally, the extreme precipitation only events were found to be connected with the frontal system of the East Asian summer monsoon. This study highlights the importance of considering the dependence between multiple flood drivers associated with certain types of the same weather systems when assessing the flood risk in coastal regions. [ABSTRACT FROM AUTHOR]

The time of rainy season onset is crucial information for policymakers, especially in densely populated regions such as the Yangtze River basin (YRB) in China. In this study, we proposed a new grid-based index to objectively detect mei-yu onset timing using reanalysis data and model predictions, and then we identified the key processes via which intraseasonal oscillation (ISO) affects the YRB mei-yu onset and its subseasonal predictability based on scale-decomposed moisture analysis. Climatologically, propagation of an ISO anticyclonic anomaly toward East China supports the moisture convergence required for rainy season onset over the YRB via interaction with the seasonal-mean moisture component. In the years of early mei-yu onset, the ISO was enhanced earlier in May and favored the moisture convergence anomaly in late May–early June, when the mei-yu started. In contrast, the enhanced ISO and associated moistening processes were observed later in June–early July in the years with delayed onset. The European Centre for Medium-Range Weather Forecasts and National Centers for Environmental Prediction models show skillful prediction of mei-yu onset at forecast lead times of 5–6 pentads, whereas the China Meteorological Administration model has limited skill of 3 pentads. The differences in model prediction skill are related to the accuracy of predicted moisture convergence anomalies induced by the ISO. The prediction bias in mei-yu onset timing (early or delayed) is also connected to bias in the occurrence timing of enhanced intraseasonal perturbations, suggesting the vital role of ISO in YRB mei-yu onset on the subseasonal time scale. [ABSTRACT FROM AUTHOR]

Anomalies in atmospheric moisture transport are critical for drought formation, with East Asian monsoon anomalies identified as the primary driver of droughts in East Asia. However, the drought mechanism for the border region between monsoon and nonmonsoon land areas remains unclear due to complex interactions between latitudinal circulation patterns. Using a Lagrangian framework, we quantify moisture supply (MS) during spring and summer drought events within the border region, specifically Northeast China (NEC). Our results reveal that decreased MS from monsoon land areas is more crucial than nonmonsoon land areas and local areas for NEC droughts, with the local water recycling playing a more substantial role in summer than in spring. On average, summer droughts are 4 times more intense than spring droughts. Contributions to MS deficits from monsoon land areas, nonmonsoon land areas, and local areas during spring (summer) droughts are 43.2% (43.8%), 25.1% (19.2%), and 22% (33.8%), respectively. The weakened atmospheric circulation from source regions to NEC contributes to over 80% of MS deficits during droughts. Atmospheric wave trains triggered by North Atlantic sea surface temperature anomalies (SSTA) gradients, along with the weakening of the East Asian subtropical westerly jet in spring and a positive phase of the polar–Eurasian teleconnection in summer, contribute to spring and summer droughts, respectively. A sustained, record-breaking positive SSTA along the western European coast from the spring to summer excited a wave train, and resulted in the extreme 2017 spring–summer drought. These findings provide valuable insights into the drought mechanism within the interaction zone of circulation systems from different latitudes. Significance Statement: The complex interactions between atmospheric circulation patterns at different latitudes have led to an insufficient understanding of the mechanisms causing extreme drought in the border region between the monsoon and nonmonsoon land areas. This study aims to investigate the drought mechanisms in the border region from the perspective of moisture tracking. Results indicate a moisture supply deficit from the monsoon region contributes the most (approximately half) to droughts, although the majority of air parcels reaching this region originate from the nonmonsoon region. The weakening of circulation contributes to more than 80% of the moisture supply deficit during droughts, far exceeding the impact of reduced evaporation from the moisture source regions. These findings are conducive to understanding the mechanisms of extreme drought. [ABSTRACT FROM AUTHOR]

The temporal distribution of precipitation is of great importance in water availability and the hydrological cycle. Existing studies, directly comparing observed and simulated precipitation amount and intensity over different temporal intervals, show that reanalyses overestimate precipitation frequency and underestimate precipitation intensity, and therefore cannot characterize extreme precipitations. In this study, relative values of precipitation (i.e., the cumulative fractions of precipitation on different percentiles of wettest hours or days to the annual total) were used to evaluate ERA5 over China during the warm seasons of from 1979 to 2015. We found that ERA5 well reproduced the relative values of daily precipitation to the annual total, although hourly results were less satisfied. The cumulative fractions at the hourly time scale at the 99th, 95th, and 90th percentiles were 15.2% ± 3.4%, 38.7% ± 6.6%, and 54.5% ± 7.4% for the gauge observations, and 9.0% ± 1.7%, 27.2% ± 4.0%, and 41.5% ± 5.0% for ERA5, respectively. ERA5 had excellent agreement with gauge observations at daily time scales: gauge observations were 13.0% ± 3.4%, 32.1% ± 5.1%, and 47.6% ± 5.7%, compared to 11.5% ± 3.7%, 30.7% ± 7.0%, and 46.0% ± 8.8% for ERA5, respectively. This difference was mainly due to the high frequency of hourly precipitation in the reanalyses, which were cancelled out at the daily scale. Gauge observations at the hourly scale showed an increase cumulative fraction at the 99th, 95th, and 90th percentiles (0.07% ± 0.03% decade−1, 0.15% ± 0.03% decade−1, and 0.17% ± 0.03% decade−1, respectively) from 1979 to 2015, but did not show an obvious trend at the daily scale. These tendencies were underestimated at the hourly scale but overestimated at the daily scale in ERA5. The results helped us to characterize the temporal distributions and variations of precipitation, and to deepen our understanding of hydrological processes and reanalyses performance. [ABSTRACT FROM AUTHOR]

Weighting models according to their performance has been used to produce multimodel climate change projections. But the added value of model weighting for future projection is not always examined. Here we apply an imperfect model framework to evaluate the added value of model weighting in projecting summer temperature changes over China. Members of large-ensemble simulations by three climate models of different climate sensitivities are used as pseudo-observations for the past and the future. Performance of the models participating in the phase 6 of the Coupled Model Intercomparison Project (CMIP6) are evaluated against the pseudo-observations based on simulated historical climatology and trends in global, regional, and local temperatures to determine the model weights for future projection. The weighted projections are then compared with the pseudo-observations in the future period. We find that regional trend as a metric of model performance yields generally better skill for future projection, while past climatology as performance metric does not lead to a significant improvement to projection. Trend at the grid-box scale is also not a good performance indicator as small-scale trend is highly uncertain. For the model weighting to be effective, the metric for evaluating the model's performance must be relatable to future changes, with the response signal separable from internal variability. Projected summer warming based on model weighting is similar to that of unweighted projection but the 5th–95th-percentile uncertainty range of the weighted projection is 38% smaller with the reduction mainly in the upper bound, with the largest reduction appearing in southeast China. [ABSTRACT FROM AUTHOR]

In China, the topography, climate, ecology, hydrology, and human environment vary greatly from southeast to northwest, and a typical natural and social environmental transition zone (i.e., comprehensive transition zone) exists near the "Hu Huanyong line," which is a famous demographic dividing line in China, known as the Hu zone. Dry and wet climate changes in the Hu zone can have a significant impact on terrestrial ecosystems and hydrological conditions, ultimately affecting human–land relations. However, there is still a lack of clear understanding of environmental changes in the context of climate change in the Hu zone. Here, a quantitative analysis of climate change and its impacts on terrestrial hydrology and ecosystems from 1951 to 2020 is presented. The results showed that there exists a significant drying trend in the Hu zone and a dramatic decrease in terrestrial water storage (TWS), indicating that the environment has become worse. Conversely, from the perspective of significant greening, the environment has improved. This contradiction is mainly due to climate change dominating the depletion of TWS, while the increase in vegetation greenness is more driven by human activities including agricultural management and ecological restoration, offsetting to some extent the negative impact of water scarcity on vegetation growth. Significance Statement: The Hu zone is a transition zone between southeast and northwest China, which is a sensitive area under climate change as well as key region for coordinated development. The purpose of this study is to reveal the long-term climate change in the Hu zone and its impacts on hydrology and ecology. Our results indicate a significant drying trend in this zone over the last 70 years, which led to a substantial reduction in water storage. However, the vegetation coverage increased due to human activities. This study provides guidance for agricultural structure adjustment and ecological protection in transition zones. Future research should focus more on the assessment and risk management of dry/wet changes in climate transition areas around the world. [ABSTRACT FROM AUTHOR]

The research and application of convolutional neural networks (CNNs) on statistical downscaling have been hampered by the fact that deep learning is highly dependent on sample size and is considered to be a black-box model. Therefore, a CNN model with transfer learning (CNN-TL) is proposed to study the pre-rainy season precipitation of South China. First, an augmented monthly dataset is created by sliding a fixed-length window over the daily circulation field and precipitation data for the entire year. Next, a base CNN network is pretrained on the augmented dataset, and then the network parameters are tuned on the actual monthly dataset from South China. Then, guided backpropagation is conducted to obtain the distribution regions of the key features and explain the net. The coefficient of determination R2 and root-mean-square error (RMSE) show that the CNN-TL model has higher explanatory power and better fitting performance than the feature extraction–based random forest. In comparison with the base CNN, the transfer learning approach can improve the explanatory power of the model by 10.29% and reduce the average RMSE by 6.82%. In addition, the interpretation results of the model show that the critical regions are primarily South China and its surrounding areas, including the Indochina Peninsula, the Bay of Bengal, and the South China Sea. Furthermore, the ablation experiments and composite analysis illustrate that these regions are very important. Significance Statement: To mitigate the challenges posed by small sample sizes and the transparency of deep learning in downscaling problems, we propose a convolutional neural network based on sample augmentation and transfer learning to study the monthly precipitation downscaling problem during the preflood period in South China. In comparison with random forests and conventional convolutional neural networks, our model achieves an optimal interpretation rate and stability. In addition, we explore the interpretability of the model using guided backpropagation to find the distribution of key features within the large-scale circulation field, thus increasing the credibility of the model. [ABSTRACT FROM AUTHOR]

Typhoon Hagupit (2020), which formed unexpectedly close to land, posed great challenges for forecasters. During its genesis, there was a westward-moving upper-tropospheric cold low (UTCL) to its north. This study investigated the impact of this UTCL on the genesis process using numerical simulations. In the semi-idealized experiment with this UTCL removed (run-Rcold), pre-Hagupit develops faster, but its track drifts southward in the later stage compared with the control experiment (run-cnl). In the experiment with enhanced UTCL (run-Ecold), the simulated track is similar to that in run-cnl, but pre-Hagupit does not develop into a tropical storm. In run-cnl and run-Ecold, the environmental vertical wind shear is larger than that in run-Rcold in the first 2 days, and the simulated pre-Hagupit experiences two prominent dry-air intrusions in the middle and upper troposphere. At the second intrusion, when the weakened UTCL has moved within 2° of pre-Hagupit, the convection in both experiments decays significantly, and the development of the midlevel vortex begins to lag behind that in run-Rcold, and so does the vertical alignment of the low- and midlevel vortices. The UTCL influences the movement of pre-Hagupit by modifying the large-scale steering flows, especially those above 600 hPa. In run-Rcold, due to the absence of the northward component of wind fields related to the UTCL circulation, pre-Hagupit starts to move west-northwestward instead of northwestward as in run-cnl and run-Ecold. Significance Statement: Typhoon Hagupit (2020) got named near the 24-h warning line of China. TCs crossing this line are considered to have a significant impact on China within 24 h, or may make landfall within 24 h. After formation, Hagupit rapidly intensified to its peak intensity of 42 m s−1 in 48 h, and made landfall in Zhejiang Province at severe typhoon strength after another 8 h. Both the unexpected genesis and the rapid intensification of Hagupit posed great challenges for forecasters and deserve further investigation. This study focused on the genesis process. We noticed that there was a westward-moving upper-tropospheric cold low (UTCL) in the north during the formation of Hagupit. We have conducted three numerical experiments to investigate the impact of this UTCL on the genesis of Hagupit, and found that the UTCL is an important factor affecting the genesis process. It is detrimental to the vortex development. If the UTCL is removed, Hagupit will develop into a tropical storm more quickly, but its potential landfall point of Hagupit will shift from the southeast coast of China to the Philippines. [ABSTRACT FROM AUTHOR]

Precipitation in southern China during April–June 2022 was the highest since 1961. Anthropogenic forcing has reduced the probability of 2022-like Rx30day precipitation by about 45% based on CMIP6 simulations. [ABSTRACT FROM AUTHOR]

The effects of various strategies aimed at simultaneously promoting environmental conservation and human development are closely related to sustainable development regionally and globally. However, although the effects of many such strategies have been evaluated by ecologists and sociologists separately, their ability to simultaneously meet these two anticipated goals (i.e., environmental conservation and human development) at the fine spatial scale remains unclear. To answer this fundamental but crucial question, incorporating household and forest change data, we concurrently estimated the ecological and socioeconomic effects of two world-renowned Payment for Ecosystem Services (PES) programs (i.e., the Nature Forest Conservation Program, the Grain to Green Program) and nature-based tourism in 30 protected areas across 8 provinces in China. Here we showed a trade-off between the ecological and economic effects of two PES programs, while synergistic effects exist in the ecological and economic benefits of tourism. Attributes of household and protected areas significantly influenced economic and environmental benefits as well. Our research provides new insights into the complex effects of PES programs and tourism, and crucial information to support their adequate and sustainable implementation in China and the rest of the world. Significance Statement: This work answers a fundamental but crucial question, that is, whether the policies commonly advocated to incorporate environmental conservation and human development can yield positive effects both for conservation and economic development. Our evaluation is also timely to inform some shortness (i.e., negligible economic effects, or the lack of expected positive economic benefits) and provides new insights (e.g., the implication of households and protected-areas attributes in conservation and economic outcomes) of Payment for Ecosystem Services (PES) programs and the complex effects of instruments in the context of multiple policies, particularly given the upcoming 2030 deadline for achieving the Sustainable Development goals (SDGs). We expected that implications in this study can provide important lessons for these two instruments, other PES programs, and other conservation and development instruments to support their adequate and sustainable implementation in China and beyond and to contribute to the achievement of relevant SDGs in the remaining years. [ABSTRACT FROM AUTHOR]

In this study, downscaling, ensemble data assimilation, time lagging, and their combination were used to generate initial condition (IC) perturbations for 12-h convection-permitting ensemble forecasting for heavy-rainfall events over South China during the rainy season in 2013–20. These events were classified as weak- and strong-forcing cases based on synoptic-scale forcing during the presummer rainy season and as landfalling tropical cyclone (TC) cases. This study investigated the impacts of various IC perturbation methods on multiscale characteristics of perturbations and the forecast performance for both nonprecipitation and precipitation variables. These perturbation methods represented different source IC uncertainties and thus differed in multiscale characteristics of perturbations in vertical structures, horizontal distributions, and time evolution. The combination of various IC perturbation methods evidently increased perturbations or spreads of precipitation in both magnitude and location and thus improved the forecast-error estimation. Such an improvement was most and least evident for TC cases during the early and late forecasts, respectively, and was more evident for strong- than weak-forcing cases beyond 6 h. The combination of various IC perturbation methods generally improved both the ensemble-mean and probabilistic forecasts with case-dependent improvements. For heavy rainfall forecasting, 1–6-h improvements were most prominent for TC cases in terms of discrimination and accuracy, while 7–12-h improvements were least prominent for weak-forcing cases in terms of reliability and accuracy. In particular, the improvements in predicting weak-forcing cases increased with spatial errors. In contrast, for strong-forcing cases, the improvements were least and most prominent before and beyond 6 h, respectively. Significance Statement: Precipitation forecasting for heavy-rainfall events over South China in the rainy season is still challenging due to large uncertainties. Convection-permitting ensemble forecasting is expected to address such uncertainties to improve forecasts of heavy rainfall. However, it is not yet clear how to optimally design convection-permitting ensembles by implementing perturbations in initial conditions (ICs). This study investigates the impacts of various IC perturbation methods on convection-permitting ensemble forecasting over South China in the rainy season. Various IC perturbation methods show discrepant multiscale characteristics of perturbations, which generally complement each other when these perturbations are combined. The added values of combining various IC perturbation methods in forecasting are confirmed for most variables. However, such values are case dependent, with the largest values for tropical cyclone cases during the early forecasts and for the presummer rainy season cases with strong synoptic-scale forcing during late forecasts. Thus, it is still essential to further improve the combination of various types of IC perturbation methods. [ABSTRACT FROM AUTHOR]

Observations and model simulations have confirmed that the higher percentiles of precipitation intensity of all wet days have increased with the local daily dewpoint temperature at the Clausius–Clapeyron (C-C) rate of 7% °C−1 using the binning technique over China. It is uncertain whether the binning scaling rates will remain constant in the future climate. Here, we use daily precipitation and dewpoint temperature (DPT) over China from 20 CMIP6 models to examine and project the change of binning scaling of precipitation extremes. The results of the study suggest that the multimodel ensemble median (MEM) generally captures the spatial distribution of binning scaling rates and binning curves in different climate zones across China. The binning scaling rates have stability in the future but with a large increasing trend (more than 30%) in southeast China in the far future (2061–2100) under the SSP5-8.5 scenario. CMIP6 models project that both the peak of extreme precipitation and the temperature for the plateau mountain zone will increase in the far future under the SSP5-8.5 scenario, implying that the peak structure does not provide an upper limit for future precipitation extremes. Significance Statement: The binning scaling rates describe the relationship between precipitation extremes and temperature based on their day-to-day variations. However, it is still unclear whether the binning scaling rates of daily precipitation extremes with daily temperature will change in the future. We use 20 CMIP6 models to project the scaling rates of precipitation extremes with dewpoint temperature over China in the future. The results show that the sensitivity of precipitation extremes to dewpoint temperature will generally be stable, except that southeast China shows an increasing trend of up to 30%. Moreover, the peak of extreme precipitation and the temperature will increase with warming in the plateau mountain zone. Our results have implications for precipitation prediction and risk assessment. [ABSTRACT FROM AUTHOR]

The Indian Ocean basin (IOB) mode is the dominant mode of the interannual sea surface temperature (SST) variability in the Indian Ocean, with the Indian Ocean dipole (IOD) as the second mode. An IOB event normally occurs after an El Niño or a concurrent IOD–El Niño event, the dynamics of which are traditionally believed as forced by ENSO through the Walker circulation anomalies over the tropical Indian Ocean. A strong IOB in 2020 took place after the strongest 2019 IOD on record but independent of El Niño, which challenges the traditional atmospheric bridge dynamics of the IOB event. In this study, the dynamics of the 2020 IOB event are investigated using the numerical seasonal climate prediction system of the National Marine Environmental Forecasting Center of China. It is found that the initialization of the Indian Ocean subsurface temperature during the 2019 IOD event has led to the outburst of the 2020 IOB event successfully, the dynamics of which are the propagation and the western boundary reflection of the equatorial and off-equatorial Rossby waves, inducing heat content recharge over the tropical Indian Ocean upper thermocline. In comparison, experiments of SST initialization over the tropical Indian Ocean, with the subsurface temperature in a climatological state, were unable to reproduce the onset of the 2020 IOB event, suggesting that the local air–sea interaction within the Indian Ocean basin is of secondary importance. The numerical experiments suggest that the thermocline ocean wave dynamics play an important role in forcing the IOB event. The revealed thermocline dynamics are potentially useful in climate prediction associated with IOB events. [ABSTRACT FROM AUTHOR]

An atmospheric river (AR) is a highly accumulated line of moisture, which often preconditions heavy rainfall events. Considering the short-lived nature of ARs, we aim to understand their characteristics over the western North Pacific (WNP) in boreal summer, focusing on the difference between the post–El Niño (pEN) and non-post–El Niño (npEN) cases in weekly segments rather than the seasonal mean. We found that during AR-active weeks in pEN summers, a meridional dipole pattern of anomalous geopotential height with a more westward-extended WNP subtropical high dominates, favoring AR activity over southern China to the central Pacific. In npEN summers, in contrast, a zonal dipole pattern with an anomalous low over southeast China and an anomalous high over subtropical WNP is pronounced during the AR-active weeks, which is accompanied with warm sea surface temperature anomalies and greater downward shortwave radiation to the southeast of Japan. Such anomalous circulation leads to enhanced AR occurrence over the northern South China Sea (SCS) to Japan. The above differences in the AR features between the two cases appear as the distinct regionality in the AR-related rainfall over eastern China and Japan. Temporal decompositions reveal that both seasonal mean and submonthly variations play dominant roles in the anomalous circulation during AR-active weeks in pEN summers, while the submonthly variations are more pronounced in npEN summers. Further analysis also suggests that tropical cyclones could impact AR occurrence in both cases with different patterns of atmospheric responses, inducing remarkable AR enhancement over the northern SCS in npEN summers. [ABSTRACT FROM AUTHOR]

The effects of the response characteristics of the Fengyun-4A Lightning Mapping Imager (LMI) on its detection capability were studied using the raw event data of LMI in 2020. The simultaneous observation data of the Lightning Imaging Sensor (LIS) on the International Space Station (ISS) were used to evaluate the LMI detection capability. The results reveal that the minimum detectable radiance of lightning events in the 16 subregions of LMI has shown regional differences, with the southern subregions lower than the northern subregions, indicating that the southern ones are more conducive to the identification of events. The diurnal variation of the detectable event radiance in all subregions presents the main peak around noon, which comes from the influence of the bright background and varies largely in different subregions depending on the subregions' response capability. The overall high values and regional differences of flash properties observed by LMI also show strong correlation with the variation of the minimum detectable radiance of events. Moreover, it is found that the southwest subregions have the highest coincidence ratio (CR) with ISS LIS, followed by the southeast subregions and the northeast subregions, and the northwest subregions have the lowest CR, which is closely related to the response of each subregion. The LIS flashes that can be detected by LMI are brighter, larger, and last longer compared to the total LIS flashes. The findings in this study will help explain the inconsistency of the LMI detection capability and promote the LMI data processing associated with pixel energy distribution. Significance Statement: The Fengyun-4A (FY-4A) Lightning Mapping Imager (LMI) is the first geostationary satellite-borne lightning imager developed in China, which has the ability to continuously observe lightning within a large field of view. However, in the application of the data, it was found that the detection capability of the LMI differed significantly in different regions and exhibited diurnal variations, which may be related to the response characteristics of the LMI detector. This study reveals the effect of the response characteristics of the LMI detector on its detection results. The findings will help improve the usability of LMI data and improve the processing of these data to adjust the observation inconsistency. [ABSTRACT FROM AUTHOR]

In this study, the Flexible Particle Dispersion Model (FLEXPART) is applied to analyze the moisture sources of Northeast China precipitation from March 1979 to February 2018. The results show that there is mainly one particle aggregation channel in winter, namely the eastern Europe–Siberia–Lake Baikal–Northeast Asia channel (the western channel). In comparison with winter, there are two extra channels in summer, namely the Indochina Peninsula–South China Sea–East China channel (the southern channel) and the Philippine Sea–Ryukyu Islands channel (the southeastern channel). From the long-term mean, the Siberia–Mongolia–Xinjiang region (SMX) is the most dominant moisture source of Northeast China precipitation in all seasons. As for the moisture contribution rate of each source region to Northeast China precipitation, there is a seesaw interannual relationship between SMX and other source regions. The moisture from Central and East China is critical to the interdecadal shift of Northeast China summer precipitation. This interdecadal shift is related to the moisture transport from low latitudes to Northeast China, which is modulated by the positive phase of the Pacific decadal oscillation and the negative phase of the Atlantic multidecadal oscillation. [ABSTRACT FROM AUTHOR]

The abundant precipitation over Southern China during the pre–flood season (PFS) [i.e., April–June (AMJ)] has important socioeconomic impacts on this densely populated region. Using observational and reanalyzed datasets, this study explores how El Niño affected the subsequent PFS precipitation over Southern China during 1961–2020. The results show that the El Niño–related anomalies in sea surface temperature forced a northwestern Pacific anomalous anticyclone (NPAAC) in the decaying AMJ. This NPAAC featured southwesterly wind anomalies in its northwestern flank, which could transport moisture from the South China Sea, and accompanying the NPAAC there was abnormal descending motion over the tropical western Pacific, resulting in weakened regional Hadley circulation with abnormal ascending motion over subtropical East Asia. Before the 1990s, this abnormal ascending motion was located mainly to the east of Southern China with insignificant impacts on the PFS precipitation there. In contrast, after the early 1990s, El Niño–related warm sea surface temperature anomalies were stronger and longer-lasting with westward extension. This enhanced the NPAAC with a decadal westward extension, and consequently, the anomalous regional Hadley circulation was more evident over Southern China after the early 1990s during the El Niño decaying AMJ, causing strong abnormal upward motion and excessive precipitation there. The present results emphasize an enhancing influence of El Niño on the subsequent PFS precipitation over Southern China since the early 1990s, offering better understanding of the interannual precipitation variability over Southern China and with important implications for regional seasonal climate prediction. Significance Statement: The precipitation over Southern China during the pre–flood season (PFS) contributes between 40% and 50% of the local annual precipitation, with severe floods occurring frequently and tremendous socioeconomic impacts on the region, including on its agriculture, water resources, food security, ecosystems, disaster mitigation, infrastructure construction, and human health. This study reveals a decadal enhancement in the effect of El Niño on the subsequent PFS precipitation over Southern China since the early 1990s. This is ascribed to a decadal enhancement of El Niño–related warm sea surface temperature anomalies over the equatorial central Pacific, as a result of El Niño warming occurring more frequently over the central Pacific against the backdrop of global warming. To the extent that the central Pacific El Niño events would occur even more frequently for the projected future warming scenarios, a stronger effect of El Niño on the PFS precipitation over Southern China would be expected, implying potentially enhanced seasonal predictability of the regional climate in the future. [ABSTRACT FROM AUTHOR]

This paper presents a detailed analysis of how future climate change may affect water availability in a typical arid endorheic river basin, the Heihe River basin (HRB), in northwest China. The analysis is based on the improved Soil Water Assessment Tool (SWAT), which is calibrated and validated with historical streamflow data from the upper HRB and is used to predict future hydrological responses. Six general circulation models (GCMs), under two emission scenarios (RCP4.5 and RCP8.5), are downscaled to construct future climate change scenarios. The results suggest that the climate of the upper HRB will likely become warmer and wetter in the near future (2021-50), with the largest increase in precipitation occurring in the summer. Correspondingly, the basinwide evapotranspiration, snowmelt, and runoff are projected to increase over the same period. The mean temperature in the near future is projected to rise, relative to the recent 30 years (1981-2010), by 1.2°-1.7°C under scenario RCP4.5 and by 1.4°-2.1°C under scenario RCP8.5. The mean precipitation is projected to increase by 10.0%-16.6% under scenario RCP4.5, and by 10.5%-22.0% under scenario RCP8.5. The mean values of evapotranspiration, snowmelt, and runoff are expected to increase by 14.2%, 4.3%, and 11.4%, respectively, under scenario RCP4.5 and to increase by 18.7%, 5.8%, and 12.8%, respectively, under scenario RCP8.5. Though the model simulations forecast an increase in streamflows in the headwater region of the HRB, future water availability varies significantly over space and time. The findings of this study will help to frame more effective water management strategies for the HRB under changing climatic conditions. [ABSTRACT FROM AUTHOR]