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Currently, the opportunistic method to estimate rainfall using commercial microwave links (CMLs) has been shown as an efficient way to complement traditional instruments in terms of spatial‐temporal resolution and coverage. In this paper, we collected data from 26 CMLs in Jiangyin City, Jiangsu Province, and conducted experiments on rainfall field reconstruction and nowcasting. First, the raw CML data were processed to invert the path‐averaged rainfall intensity. Second, the algorithms of inverse distance weighting (IDW) and ordinary kriging (OK) interpolation were employed to reconstruct the rainfall field. Then a 10‐min prediction of the rainfall field was achieved using a nowcasting model based on the long short‐term memory neural network and a setup window was introduced to improve the prediction performance of the first few minutes. The reconstruction results show that the average correlation coefficient (ACC) and the average root mean square error (ARMSE) between the IDW‐based results and daily cumulative rainfall from rain gauges (RGs) are 0.89 and 8.69 mm, respectively, while the ACC and ARMSE between the OK‐based results and RG data are 0.89 and 9.13 mm, respectively. The nowcasting results show that the ACC between the prediction results with a 5‐min setup window and the IDW‐retrieved rainfall fields can reach 0.91 at the first minute and gradually decrease to 0.20 within 10 min. Furthermore, the model has better nowcasting performance for stratiform precipitation and mixed precipitation compared to convective precipitation. Plain Language Summary: Accurate and real‐time rainfall monitoring and forecasting are of great significance for disaster prevention and control, agriculture, meteorological research, and related fields. However, traditional rainfall measurement methods are insufficient for meeting the demands of comprehensive precipitation observations because of poor spatial‐temporal resolution and limited coverage. Currently, measuring rainfall using additional microwave attenuation caused by raindrops has been shown as an efficient way to complement traditional instruments. Based on commercial microwave link (CML) rainfall measurement technology, this paper carries out rainfall field reconstruction and nowcasting experiments in Jiangyin City, China. The results show the CML network enables accurate rainfall field reconstruction and nowcasting. Key Points: Based on the commercial microwave link (CML) rainfall monitoring network in Jiangyin, China, accurate rainfall inversion by CMLs is achievedThe two‐dimensional rainfall fields are accurately reconstructed using the inverse distance weighting and ordinary kriging interpolation algorithmsA long short‐term memory‐based rainfall field nowcasting model is proposed to achieve 10‐min continuous predictions of rainfall fields [ABSTRACT FROM AUTHOR]

Accurate rainfall measurement with a precise spatial and temporal resolution is essential for making informed decisions during disasters and conducting scientific studies, particularly in regions characterized by intricate terrain and limited coverage of automated weather stations. Retrieval of precipitation with satellite is currently the most effective means to obtain precipitation over large‐scale areas. The key to enhancing the accuracy of precipitation estimation and forecasting in regions with complex terrain lies in effectively integrating satellite data with topographic information. This paper introduces a deep learning approach called AttUnet_R_SFT that utilizes high temporal, spatial, and spectral resolution data obtained from the Fengyun 4A satellite, and incorporates the Deep Spatial Feature Transform (SFT) layer to incorporate geographical data for estimating half‐hourly precipitation in northeastern China. We assess it by compared to operational near‐real‐time satellite precipitation products demonstrated to be successful in estimating precipitation and baseline deep learning models. According to the experimental findings, the AttUnet_R_SFT model outperforms practical precipitation products and baseline deep learning models in both identifying and estimating precipitation. The main enhancement of the model performance is shown in the windward slope of the Greater Khingan Mountains as a result of the successful incorporation of geographical data. Hence, the suggested framework holds the capability to function as a superior and dependable satellite‐derived precipitation estimation solution in regions characterized by intricate terrain and infrequent rainfall. The findings of this study indicate that the utilization of deep learning algorithms for satellite precipitation estimation shows potential as a fruitful avenue for further research. Plain Language Summary: A deep learning model named AttUnet_R_SFT is proposed, which use high temporal, spatial and spectral resolution data from the Fengyun 4A satellite, and combines with the Deep Spatial Feature Transform (SFT) layer to input geographic information for half‐hourly precipitation estimation in the complex terrain region represented by northeast China. The model can provide a reference for improving the performance of precipitation estimation in areas with complex topography. Key Points: A deep‐learning model is proposed to effectively fuse satellite multispectral data of the Fengyun 4A satellite, with topographic informationHalf‐hourly precipitation is estimated with higher temporal resolution, which is closer to the operational needs of weather forecastingAs precipitation in the study area is a non‐high‐frequency event, data enhancement is attempted to use and obtain effective results [ABSTRACT FROM AUTHOR]

Satellite infrared detectors cannot penetrate clouds, especially precipitating clouds. Improving precipitation estimation accuracy based on infrared brightness temperature has always been important but challenging. In this paper, based on the infrared brightness temperature of the Advanced Geosynchronous Radiation Imager (AGRI) onboard China's Feng‐Yun 4A satellite, we develop and evaluate a new precipitation estimation method. First, using static data, physical characteristics of clouds, cloud image texture features, temporal motion features, and AGRI infrared channel brightness temperature, we construct features for a machine learning model. Then, we develop precipitation estimation methods. Precipitation is estimated in two steps: classification and regression. We employ a random forest classification model to identify whether there is precipitation in a given field of view. If there is precipitation, a multi‐model ensemble regression learning method is used to estimate the areas with this precipitation. The ensemble learning method uses convex optimization to integrate prediction results based on the optimization of hyperparameters of five basic models (i.e., those of random forest, XGBoost, LightGBM, decision tree, and extra tree models). Furthermore, two regression stacking ensemble models—the Least Absolute Shrinkage and Selection Operator (herein referred to as Stacking1‐LASSO) and K‐nearest neighbor (herein referred to as Stacking2‐KNN)—are used to predict the results of the aforementioned basic models. The results of basic models are used as inputs of these two stacking models. Finally, based on the Integrated Multi‐satellitE Retrievals for GPM (IMERG) precipitation product and rain gauge precipitation data, we conduct precipitation estimation experiments and evaluate our methods. The results show that ensemble learning models have greater accuracy in estimating precipitation than the basic models. When using IMERG precipitation as the target precipitation, ensemble learning models can estimate the central area of heavy precipitation during typhoons Ampil and Maria. The ensemble learning estimation effect is better than that of Stacking2‐KNN. Moreover, when rain gauge data is used as the target precipitation, ensemble learning can also estimate the center of heavy precipitation and with good consistency with recorded satellite brightness temperature data. Key Points: A convex optimization weighted and stacked ensemble learning method for satellite data estimation of precipitation is developedNear real‐time precipitation estimates can be obtained throughout the day using only geographic information and Advanced Geosynchronous Radiation Imager infrared brightness temperature dataThe proposed ensemble learning method can estimate the central areas of typhoons and short‐term heavy rainfall [ABSTRACT FROM AUTHOR]

This study proposes a novel method, which is a U‐shaped convolutional neural network that combines non‐local attention mechanisms, Res2net residual modules, and terrain information (UNR‐Net). The original U‐Net method and the linear regression (LR) method are conducted as benchmarks. Generally, the UNR‐Net has demonstrated promise in performing a 10× downscaling for daily 2‐m temperature over North China with lead times of 1–7 days and shows superiority to the U‐Net and LR methods. To be specific, U‐Net and UNR‐Net demonstrate higher Nash‐Sutcliffe Efficiency coefficient values compared to LR by 0.052 and 0.077, respectively. The corresponding improvements in pattern correlation coefficient are 0.013 and 0.016, while the root mean square error values are higher by 0.22 and 0.338, respectively. Additionally, the structural similarity index metric is higher by 0.033 and lower by 0.015. Furthermore, regions with significant errors are primarily distributed in complex terrain areas such as the Taihang Mountains, where UNR‐Net exhibits noticeable improvements. In addition, the 12 components‐based error decomposition method is proposed to analyze the error source of different models. Generally, the smallest errors are observed during the summer season and the sequence error component is proven to be the main source error of 2‐m temperature forecasts. Furthermore, UNR‐Net consistently demonstrates the lowest errors among all 12 error components. Therefore, combining the numerical weather prediction model and deep learning method is very promising in downscaling temperature forecasts and can be applied to routine forecasting of other atmospheric variables in the future. Plain Language Summary: This research proposes a new method for downscaling using deep learning. The method uses a specific type of neural network called UNR‐Net, which combines attention mechanisms, residual modules, and terrain information. The performance of UNR‐Net is compared to two other methods: U‐Net and LR. In the study, UNR‐Net shows promise in performing a 10× downscaling of the daily 2‐m temperature in North China. The UNR‐Net demonstrates the best overall performance among all the comprehensive indicators (NSE, pattern correlation coefficient, root mean square error, and structural similarity index metric). Errors in the predictions are mainly found in complex terrain areas like the Taihang Mountains, but UNR‐Net shows noticeable improvements in these regions. The study also proposes a 12 components‐based error decomposition method to analyze the error sources of different models. All in all, it is found that the smallest errors are observed during the summer season and the main source error is the sequence error component. Additionally, when considering lead times of 1–7 days, UNR‐Net consistently shows the lowest errors among all 12 error components. Based on these findings, combining numerical weather prediction models with deep learning methods holds great promise for generating high‐resolution temperature forecasts. Key Points: This paper presents a novel deep learning downscaling method, UNR‐Net, capable of downscaling daily 2‐m temperature by a factor of 10The overall performance of the UNR‐Net method surpasses the U‐Net method and linear regression methodThe 12 components‐based error decomposition method is proposed to analyze the error source of different models [ABSTRACT FROM AUTHOR]

In network real‐time kinematic positioning of multi‐reference station, the spatial and temporal distribution of tropospheric delay is affected by both horizontal and elevation. The traditional modeling strategy of regional troposphere takes more consideration of the horizontal factor, and the incomplete consideration of the elevation factor will lead to the problem of reduced modeling accuracy, especially in the face of the scene with large regional height deviation. Based on the traditional linear interpolation method (LIM), a simple and effective height‐deviation compensation‐based linear interpolation method (HCLIM) for regional tropospheric is proposed. The modeling accuracy of troposphere and the positioning accuracy of user RTK in large height deviation region are significantly improved. The method was verified based on six experimental subnets with large height deviations from a provincial continuously operating GNSS reference stations network in central China. The results showed that: For GPS satellite modeling, compared with the traditional LIM method, the average modeling accuracy improvement rate of HCLIM method is (84.5%, 75.5%, 59.3%, 26.7%) in the elevation angle range of (10–30°/30–40°/40–50°/50–90°). For BDS satellite, the average modeling accuracy improvement rate of HCLIM method in the above four elevation angles is (83.3%, 70%, 50%, 23.5%). For the positioning performance of user RTK, The horizontal positioning accuracy and RTK fixing rate were similar under the two methods, while HCLIM method showed only slight improvement. However, in the U direction, LIM method showed obvious systematic bias, while HCLIM method showed consistent positioning accuracy, which was improved to 82.8% compared with LIM method. Plain Language Summary: In GNSS relative positioning, the positioning performance of user largely depends on the extent to which atmospheric deviations (ionospheric delay or tropospheric delay) are eliminated or weakened. Compared with the ionosphere, the temporal and spatial characteristics of troposphere are significantly different. It is part of the lower atmosphere, extends from the Earth's surface to an altitude of about 20 km, and contains most of the atmospheric water vapor. The delay of satellite signals through the troposphere is affected by both horizontal and elevation. The traditional tropospheric regional modeling strategy only uses plane coordinates for interpolation, ignoring the influence of elevation factors or the consideration of height‐deviation is not perfect. When the height difference between the reference station and the rover station is large, the modeling accuracy will obviously be reduced. The large modeling residual may bring systematic deviation to the user coordinates, especially the elevation direction component. In this paper, we propose a regional tropospheric linear interpolation method (HCLIM) considering height deviation compensation. This method is simple and effective, The modeling accuracy of troposphere and the positioning accuracy of user RTK in large height difference region are significantly improved. Key Points: A simple and effective height‐deviation compensation‐based linear interpolation method (HCLIM) for regional tropospheric is proposedThe HCLIM method greatly improves the tropospheric modeling accuracy of each elevation angle range in the large height difference site areaThe HCLIM method significantly improves the systematic bias in elevation direction of user RTK positioning due to modeling residuals [ABSTRACT FROM AUTHOR]

Historically, the long wavelength side (6–7.5 μm) of the mid‐infrared water vapor absorption band has been used for imaging from the geostationary perspective. This began with the 6.4 μm band on Europe's Meteosat‐1. While geostationary sounders for moisture profiling, including China's hyperspectral resolution infrared sounder and a planned sounder from Europe, are or will be measuring the short wavelength side of the water vapor band, this is not the case for geostationary imagers. Shorter wavelength (5–6 μm) spectral bands for imaging applications should be considered for observing moisture in the mid and lower troposphere because of several potential advantages offered by this spectral range. The short wavelength side of the water vapor band contains fewer additional absorbing gases that overlap the water vapor absorption lines. In addition, the shorter wavelengths would show less diffraction blurring which could enable finer spatial resolution for turbulence detection. This study considers some of the differences and potential advantages of the spectral information in the short wavelength side of the water vapor absorption band from the perspective of geostationary imaging. For dry conditions land heating could impact the qualitative use of observed brightness temperatures at 5.1 μm, while the solar reflection component over most clear‐sky scenes is small for a 5.1 μm band and essentially zero for a 5.6 μm band. Plain Language Summary: Long before images are sent back to Earth from satellites, the needed spectral bands have to be specified. The band specification includes attributes such as the wavelength centers, widths, and signal‐to‐noise ratio. This paper investigates the rationale for including data from a region of the electromagnetic spectrum that has not been used for imaging from geostationary orbit. If implemented, these short wavelength water vapor bands would provide unique information regarding low‐level moisture and mid‐level turbulence. This paper quantifies the expected reflected component from the sun and the influence of land heating on observations, both of which are relatively larger at shorter wavelengths. Key Points: Historically, the long wavelength side of the mid‐infrared water vapor absorption band has been used for imagingShort wavelength (near 5 μm) spectral bands should be considered, especially with finer spatial resolutionsOver most clear‐sky surfaces, the reflective component ranges from small to zero for 5.1 and 5.6 μm bands [ABSTRACT FROM AUTHOR]

Both long‐term but small number of national stations and short‐term but large number of regional stations have been frequently used to study the extreme hourly precipitation (EXHP) in China. However, few studies focus on the differences of the two for revealing the features of EXHP. In this study, the characteristics of EXHP in Zhejiang Province are investigated using three rainfall data sets at three threshold criteria. The comparison between different data sets shows that increasing the station density can better reflect the climatic spatial distribution of EXHP thresholds if long‐term data is absent. The majority of EXHP can be classified into four weather types: the southwesterly wind type (30.7%–48.5%), the trough type (12.2%–23.6%), the tropical cyclone (TC) type (11.4%–17.5%) and the easterly wind type (4.9%–17.9%). The selection of stations is more sensitive to the proportions of the four weather types than the statistical years and threshold criteria. The monthly and diurnal variations of EXHP, as well as their differences revealed by the three data sets, are varied by weather type. Only using national stations cannot distinguish the spatial differences between the TC type and the easterly wind type, and there is an underestimation for southwesterly wind type and trough type in the mountainous area of southwest Zhejiang. The statistical year and station height are the main reasons for the differences in the duration of EXHP events calculated by different data sets, with the TC type having the largest effect and the southwesterly wind type the smallest. Plain Language Summary: Station data is the most common and reliable data for studying the characteristics of precipitation in China. There are two sources of station data available, that is, national station data with long time series but sparse and regional stations with short time series but numerous. However, few studies have compared the differences between these two sources of data for revealing the characteristics of extreme hourly precipitation (EXHP). In this study, the possible impact of different data sets for revealing the characteristics of EXHP under four weather types are investigated in Zhejiang Province. Zhejiang is located on the southeast coast of China with a long coastline and many mountains. Results suggest that increasing the station density can better reflect the spatial distribution of the EXHP thresholds if long‐term data is absent. EXHP events with strong synoptic‐scale forcing are more sensitive to the results obtained by different data sets. As the national stations are mostly located in the plains, the high frequency centers of EXHP are biased in each weather type if only using national stations, especially in southwest Zhejiang and near the coast. In summary, this paper provides additional perspectives for understanding the characteristics of regional EXHP by using the most common observations. Key Points: The selection of stations is sensitive to the proportions of the four main weather types in Zhejiang where extreme hourly rainfall occursExtreme hourly precipitation events with strong synoptic‐scale forcing are more sensitive to results obtained using different data setsThe sparse national stations located on the plains could hardly reflect the spatial distribution of extreme rainfall in mountainous Zhejiang [ABSTRACT FROM AUTHOR]

In the search for precursors to earthquakes, correlation has been found between geochemical characteristics of soil gases and seismic activity. In this paper we present evidence that seismic waves can trigger emission of soil radon (Rn) and carbon dioxide (CO2). An active experiment was performed in two fault zones in China, the Annighe fault in Sichuan province and the Xiadian fault in Heibei province. An active seismic source was used to generate seismic waves at 10 m depth in wells within bedrock. Rn and CO2 detectors were placed around the wells at a distance of ∼1 m for observing the effects of the seismic waves on the emission of the gases. The observations confirm that the seismic waves have a significant and direct effect on the concentration and flux of soil radon and carbon dioxide. When the seismic events were triggered, the observed concentrations of Rn and CO2 immediately increased and reached peak values within 5–50 min and 30–60 min, with corresponding increases of Rn and CO2 concentrations by 10.5%–238.7% and 3.1%–54.1%, respectively. The measured concentrations and flux of CO2 and Rn after the passage of the seismic waves showed strong correlation, confirming the suggestion that CO2 is the carrier gas for Rn. To the best of our knowledge this is the first direct, in‐situ measurement of gas emission caused by the passage of seismic waves and provides important constraints for better understanding of geochemical earthquake precursors. Plain Language Summary: Observed geochemical properties of soil gases migrated from the deep Earth can be used to survey seismicity, volcanic activity and emission of greenhouse gases. However, due to difficulty in natural earthquake prediction, most observations on the geochemical effects linked to seismic activity are conducted after or away from naturally occurring events, with the effects normally inferred from seismic parameters. In this study, an active seismic source based on methane gaseous detonation was employed to artificially produce seismic events along two fault zones, and in‐situ measurements of concentrations and flux of radon (Rn) and carbon dioxide (CO2) were conducted. These observations showed strong correlation between concentrations and flux of CO2 and Rn after the events, in agreement with the hypothesis that CO2 is the carrier gas for Rn in tectonically active settings. Key Points: Soil CO2 and Rn emission triggered by a new type active source that excited at 10 m deep well in bedrock are observed at first timeQuantitative effect of seismicity on the concentration and flux of soil CO2 and Rn are presentedEmission of CO2 and Rn after the seismicity show strong correlation and proved that CO2 is the carrier gas for Rn [ABSTRACT FROM AUTHOR]

Reconstructing the tectonic architecture of the southern Altaids is of great importance to piece together the continental growth history of the Central Asian continent. In this paper, we address the subduction‐accretion process of the Paleo‐Tianshan Ocean, one branch of the South Tianshan Ocean, in the southern Altaids, through a detailed geological and geochemical investigation of the Wulanmoren Accretionary Complex (WAC). The WAC is composed of ophiolitic mélanges, ocean plate stratigraphy mélanges, and turbiditic coherent units. In this study, we present key geological, petrological, and geochemical evidence indicating back‐arc and ocean island affinity of the igneous rocks of the WAC. Zircon U‐Pb dates of these rocks ages range between 380.2 ± 1.3 and 302.0 ± 5.3 Ma. The sedimentary matrix units of the mélanges were deposited in the Early Devonian (∼405 Ma) to the Late Triassic (∼222 Ma), rather than Silurian strata as previously thought. However, the ages of some detrital zircons in the sedimentary matrix are Precambrian, pointing to a complex evolution involving multiple ocean island terrains and island arcs of different ages that accreted from the mid‐Paleozoic to the Early Mesozoic. Plain Language Summary: Well‐exposed rock outcrops of the Wulanmoren Accretionary Complex (WAC) offer an ideal opportunity to solve the enigma of the evolutionary history of the Paleo‐Tianshan Ocean (PTO) of Central Asia. We report field observations, geochemistry, and geochronology of the WAC in the southern Altaids, pointing to multiple tectonic settings of ages spanning from the Early Devonian (∼405 Ma) to the Late Triassic (∼222 Ma). These reconstructions suggest closure time of the PTO occurred post‐Triassic. Based on detrital zircons provenance analysis, we propose a multi‐island ocean model to explain the tectonic evolution of the PTO in the time period studied. Key Points: The Wulanmoren Accretionary Complex was composed of ophiolitic mélanges, ocean plate stratigraphy mélanges, and coherent unitsThe Late Paleozoic MORB‐like and OIB‐type basalts, and ∼222 Ma coherent units indicate closure of the Paleo‐Tianshan Ocean occurred post‐TriassicThe southern Paleo‐Asian Ocean was an archipelago with several subduction systems which is similar to the modern southwest Pacific [ABSTRACT FROM AUTHOR]

A coupled soil temperature (ST) and moisture (SM) balance reflects a synthetic climate regime, having huge ecological impacts. This paper used ST and SM data from the European Center for Medium-Range Weather Forecasts climate reanalysis-Land and the Coupled Model Intercomparison Project Phase 6 and leaf area index (LAI) data from the Global Land Surface Satellite Product Suite. The focus was on understanding joint ST-SM changes and the resulting ecological response across China. The results show that during 2000--2020, 24.5% of the land area in China experienced a warming-drying trend resulting in a 9.7% LAI decrease, while 6.4% of the area experienced a warming-wetting trend leading to an 8.6% LAI increase. During 2015--2100, 30.6% of the land area in China will be warmer and drier, while 55.2% of the area will be warmer but wetter across three shared socioeconomic pathways (SSP126, 245, and 585). Superimposed on the long-term trends, there are also significant spatiotemporal variabilities in ST and SM on annual to decadal timescales. The LAI also showed substantial short-term fluctuations in both typical regions and ecosystems despite consistent long-term increases. Our findings suggest that ecosystems could be impaired on annual to decadal scales by adverse soil conditions in the twenty-first century, but in terms of long-term trends, ecosystems may be resilient partly because of the compensating effects of global warming and regional hydrological changes. Impact studies should thus focus more on annual to decadal soil-ecosystem anomalous events. [ABSTRACT FROM AUTHOR]

The China‐France ocean satellite (CFOSAT) has obtained synchronized observations of sea winds and sea waves for the first time in the world, providing accurate data support for the study of swells. In this paper, the accuracy of key parameters and the wave spectrum of CFOSAT surface waves investigation and monitoring (SWIM) L2 data are analyzed and evaluated using a spatial‐temporal matching criterion. The accuracies of the significant wave height (SWH), spectrum peak period (Tp) and dominant azimuth (Azi) are 0.76 m, 2.89 s and −24.02°, respectively, in the coastal area of the South China Sea (SCS) and 0.30 m, 0.97 s and 13.02° in the offshore area of the SCS. Calculated based on CFOSAT SWIM data, the annual average SWH of mixed waves in the SCS is 1.47 m, and 80% of them are between 0.5 and 3 m. There are high value areas in the central, northeastern and southwestern parts of the SCS. The average annual SWH of swells is 0.93 m, and 99% of swells are less than 2 m. A high swell SWH also exists in the central part of the SCS. Plain Language Summary: Swells are an important marine resource. Due to their long propagation distance and fast propagation speed, they can effectively forecast storm surge. They also have large energy and good stability, which are conducive to acquisition and conversion, and they have become a new method for offshore power generation. China‐France ocean satellite provides a convenient means to observe waves and to verify the accuracy of wave parameters, which is an important step for wave detection. The South China Sea, with its vast sea area and abundant swell energy reserves, is an ideal area for offshore power generation. Understanding the distribution of swell energy in the South China Sea (SCS) provides an opportunity to explore a sustainable and reliable renewable energy source. The SCS is also important and significant to the development and utilization of swell energy and for the site selection of offshore power stations. Key Points: Precision valuation of surface waves investigation and monitoring L2 data such as significant wave height (SWH), spectrum peak period (Tp), dominant azimuth (Azi) were carried out and it was determined that the data were stableThe annual average SWH in the South China Sea is 1.47 m and 80% of them are between 0.5 and 3 mWind‐swell separation was carried out, and the distribution of swell SWH shows a high value in the central part of the South China Sea [ABSTRACT FROM AUTHOR]

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

When many cities need quantitative forecasts of air quality to adjust industrial production plans and urbanization development, how to build an efficient forecast model remain a challenge. Methodology for quantitative prediction of air quality can no longer rely on single‐site observations, and thus approaches that require fewer input data and are more efficient and more reliable need to be explored. This paper proposes the principles and steps of a new model using the empirical orthogonal function (EOF) and principal component regression (PCR), which is a hybrid EOF‐PCR approach that decomposes the panel data of PM2.5 and predictors into spatial structures (EOFs) and time expansion coefficients (ECs) by EOF analysis, establishes the PCR of the ECs of PM2.5, and simulates the PM2.5 concentrations in each city by projecting the fitted ECs through EOFs. The very heart of the new model is the PCR modeling and projection. The results are presented for PM2.5 concentrations over Jiangsu Province in eastern China. The results show that this EOF‐PCR model, which is based on EC1s with a cumulative variance contribution rate above 90%, has an average prediction accuracy of 65%. The model performs best in spring and autumn, better in summer and worst in winter. Most predictors have a maximum lag of a week, and they are quite different among seasons. Considering the influence of the spatial distributions of predictors, rather than covariates at a single site, this model can reflect regional influences and effectively improve the simulation effect. Plain Language Summary: This study aims to develop a hybrid statistical model for regional forecasting of PM2.5 concentrations using EOF analysis and principal component regression, based on daily observations of PM2.5 concentrations and meteorological predictors. In the study area, the emission sources are relatively fixed, and meteorological conditions represent the main factor affecting short‐term air quality. The panel data of PM2.5 concentration and predictors are decomposed into spatial structures (EOFs) and temporal expansion coefficients (ECs) by EOF analysis. A principal component regression model was built to simulate the ECs of the PM2.5 concentrations, with the ECs of the predictors. Finally, through the inverse operation of the EOF analysis, the fitting values of the PM2.5 ECs are projected as PM2.5 concentrations at each site. The proposed model can reduce input requirements, improve computational efficiency, and emphasize the synergistic effect of meteorological factor distributional modes, rather than a point influence. Key Points: Proposed new regional forecast approach has less demand for input data, especially emissions dataForecasting accuracy was improved and reached nearly 70%, and computational efficiency and speed also increasedPM2.5 concentrations are sensitive to regional distributions of different meteorological predictors, that is, EOFs [ABSTRACT FROM AUTHOR]