China Meteorological Forcing Dataset Research Articles

Evaluation and Comparison of Five Long-Term Precipitation Datasets in the Hang-Jia-Hu Plain of Eastern China

This study analyzed the applicability of five long-term precipitation datasets in the Hang-Jia-Hu Plain of eastern China based on meteorological observation data. The accuracy of each dataset at different time scales (yearly, monthly) was analyzed. Besides, their spatiotemporal distributions and differences in precipitation event frequency were also compared. The results indicate that the high-resolution (1 day, 1 km) and long-term (1961–2019) gridded dataset for temperature and precipitation across China (HRLT) exhibited the best overall performance at the annual scale, while the long-term, gauge-based gridded precipitation dataset for the Chinese mainland (CHM_PRE) performed the best at the monthly scale. The dataset of monthly precipitation with a resolution of 1 km in China from 1960 to 2020 (HHU) and the China Meteorological Forcing Dataset (CMFD) tend to overestimate the local precipitation amounts, while the other three products are characterized by an underestimation. The mean result of the five datasets indicates a slight, statistically insignificant rise in precipitation, by 4.19 mm annually, with an overall multi-year average of 1303.28 mm. The analysis of the five datasets successfully captures the spatial precipitation patterns across the Hang-Jia-Hu Plain, characterized by higher precipitation levels in the southwest and lower in the northeast. Although the interannual variability displays general consistency, there are significant discrepancies in the interannual growth rates and the spatial distribution of significance across different regions. This study can provide a reference for the accuracy of precipitation data in the fields of hydrology, meteorology, agriculture, and ecology, facilitating the analysis of uncertainties in related research.

Comparative analysis of drought indices in the tropical zones of China

The intensity and frequency of drought are increasing in the tropical zone of China under global warming, and accurate assessment of drought severity and duration is critical for sustainable ecosystem management. Previous studies usually rely on one or more drought indices calculated from meteorological station or reanalysis data. However, the assessment results based on these drought indices are not consistent, which can be due to the differences in data sources and index parameters. In this study, we aim to identify the optimal dataset and drought index, and accurately evaluate the drought severity and drought duration in the tropical zone of China. We assessed the accuracy of five drought indices, namely Precipitation Anomaly in Percentage (PA), Relative Moisture Index (MI), Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI) and Meteorological Drought Composite Index (MCI), calculated from meteorological station data and the China Meteorological Forcing Dataset (CMFD) with respect to drought records compiled by local government. Results indicate that the drought index calculated based on meteorological station data can better match the government-compiled drought records than CMFD. MI is the optimal index for drought severity and duration assessment in study area, especially for winter-spring drought and severe drought, followed by PA. The normalized bell-shaped line of fitted precipitation in winter and spring is biased towards the less rainy side in SPI calculations, which leads to more underestimation even for officially recommended MCI, and actual water supply are also misrepresented in SPEI calculations. This study offers valuable insights for policymakers to use optimal dataset and drought index to accurately assess the drought events, and take effective measures to alleviate its impact on tropical ecosystems in China.

Evaluation and Comparison of Reanalysis Data for Runoff Simulation in the Data-Scarce Watersheds of Alpine Regions

Reanalysis datasets provide a reliable reanalysis of climate input data for hydrological models in regions characterized by limited weather station coverage. In this paper, the accuracy of precipitation, the maximum and minimum temperatures of four reanalysis datasets, the China Meteorological Assimilation Driving Datasets for the SWAT model (CMADS), time-expanded climate forecast system reanalysis (CFSR+), the European Centre for Medium-Range Weather Forecast Reanalysis (ERA). and the China Meteorological Forcing Dataset (CMFD), were evaluated by using data from 28 ground-based observations (OBs) in the Source of the Yangtze and Yellow Rivers (SYYR) region and were used as input data for the SWAT model for runoff simulation and performance evaluation, respectively. And, finally, the CMADS was optimized using Integrated Calibrated Multi-Satellite Retrievals for Global Precipitation Measurement (AIMERG) data. The results show that CMFD is the most representative reanalysis data for precipitation characteristics in the SYYR region among the four reanalysis datasets evaluated in this paper, followed by ERA5 and CFSR, while CMADS performs satisfactorily for temperature simulations in this region, but underestimates precipitation. And we contend that the accuracy of runoff simulations is notably contingent upon the precision of daily precipitation within the reanalysis dataset. The runoff simulations in this region do not effectively capture the extreme runoff characteristics of the Yellow River and Yangtze River sources. The refinement of CMADS through the integration of AIMERG satellite precipitation data emerges as a potent strategy for enhancing the precision of runoff simulations. This research can provide a reference for selecting meteorological data products and optimization methods for hydrological process simulation in areas with few meteorological stations.

Gridded Assessment of Mainland China’s Solar Energy Resources Using the Typical Meteorological Year Method and China Meteorological Forcing Dataset

The National Standard of China has recommended the typical meteorological year (TMY) method for assessing solar energy resources. Compared with the widely adopted multi-year averaging (MYA) methods, the TMY method can consider the year-to-year variations of weather conditions and characterize solar radiation under climatological weather conditions. However, there are very few TMY-based solar energy assessments on the scale of China. On the national scale, the difference between the TMY and MYA methods, the requirement of the data record length, and the impacts of the selection of meteorological variables on the TMY-based assessment are still unclear. This study aims to fill these gaps by assessing mainland China’s solar energy resources using the TMY method and China Meteorological Forcing Dataset. The results show that the data record length could significantly influence annual total solar radiation estimation when the record length is shorter than 30 years. Whereas, the estimation becomes stable when the length is greater or equal to 30 years, suggesting a thirty-year data record is preferred. The difference between the MYA and TMY methods is exhibited primarily in places with modest or low abundance of solar radiation. The difference is nearly independent of the examined data record lengths, hinting at the role of regional-specific weather characteristics. The TMY and MYA methods differ more pronounced when assessing the seasonal stability grade. A total of 7.4% of the area of China experiences a downgrade from the TMY relative to the MYA methods, while a 3.15% area experiences an upgrade. The selection of the meteorological variables has a notable impact on the TMY-based assessment. Among the three meteorological variables examined, wind speed has the most considerable impact on both the annual total and seasonal stability, dew point has the second most significant impact, and air temperature has the least. The results are useful for guiding future research on solar energy assessment in China and could be helpful for solar energy development planning.

Evaluating and Correcting Temperature and Precipitation Grid Products in the Arid Region of Altay, China

Temperature and precipitation are crucial indicators for investigating climate changes, necessitating precise measurements for rigorous scientific inquiry. While the Fifth Generation of European Centre for Medium-Range Weather Forecasts Atmospheric Reanalysis (ERA5), ERA5 of the Land Surface (ERA5-Land), and China Meteorological Forcing Dataset (CMFD) temperature and precipitation products are widely used worldwide, their suitability for the Altay region of arid and semi-arid areas has received limited attention. Here, we used the Altay region as the study area, utilizing meteorological station data and implementing the residual revision method for temperature and the coefficient revision method for precipitation to rectify inaccuracies in monthly temperature and precipitation records from ERA5-Land, ERA5, and CMFD. We evaluate the accuracy of these datasets before and after correction using bias, Taylor diagrams, and root-mean-square error (RMSE) metrics. Additionally, we employ Tropical Rainfall Measuring Mission satellite precipitation data (TRMM) as a benchmark to assess the performance of ERA5-Land, ERA5, and CMFD monthly precipitation before and after correction. The results revealed significant differences in the temperature and precipitation capture capabilities of ERA5-Land, ERA5, and CMFD in the Altay region. Overall, these data exhibit substantial errors and are not directly suitable for scientific research. However, we applied residual and coefficient revision methods. After this revision, ERA5-Land, ERA5, and CMFD showed significantly improved temperature and precipitation capture capabilities, especially for ERA5-Land. In terms of temperature, post-revision-CMFD (CMFDPR) demonstrated better temperature capture capabilities. All three datasets showed weaker performance in mountainous regions compared to plains. Notably, post-revision-ERA5 (ERA5PR) seemed unsuitable for capturing temperature in the Altay region. Concerning rain, CMFDPR, post-revision-ERA5-Land (ERA5-LandPR) and ERA5PR outperformed TRMM in capturing precipitation. CMFDPR and ERA5-LandPR both outperform ERA5PR. In summary, the revision datasets effectively compensated for the sparse distribution of meteorological stations in the Altay region, providing reliable data support for studying climate change in arid and semi-arid areas.

1990–2018年干城章嘉流域冰川物质平衡序列模拟数据集

As a result of climate change, the global glacier shrinkage was observed, and glaciers of different types have experienced distinct changes in their mass budgets. Debris-covered and/or lake-terminating glaciers have been identified in the Kanchenjunga Basin, and the dataset of their mass balance on a long-term scale could provide an important basic data for studies of specific responses of glaciers of various types to climate change. Using the rectified China Meteorological Forcing Dataset, we reconstructed the daily mass balance of glaciers in the Kanchenjunga Basin from 1990 to 2018 through the application of a surface energy balance model. We used a glacier elevation change dataset obtained through geodetic methods to validate the model estimation results, and the results align closely with previously published research results on a multi-year scale. We extracted the surface velocities of glaciers from 1989 to 2018 from the ITS_LIVE dataset and assessed their precision based on the statistical characteristics of the surface movements in the glacier-free regions. This dataset is expected to provide basic data for the studies of the responses of glaciers with different types to climate change, and can also offer valuable references for the studies of glacial hydrological effects and disasters.

Evaluation of precipitation reanalysis products for regional hydrological modelling in the Yellow River Basin

This study evaluates six precipitation reanalysis products for the Yellow River Basin using gridded rain gauge data, runoff data and the Atmospheric and Hydrological Modelling System (AHMS) simulations. The assessment begins with comparing the annual, seasonal, monthly and daily precipitation of the products with gridded rain gauge data. The AHMS is then run with each of the precipitation reanalysis products under two scenarios: one with calibrated rainfall-runoff and the other without. The simulated streamflow is then compared with the corresponding observations. It is found that non-gauge-corrected products tend to overestimate precipitation, especially for mountainous regions. Amongst the six products evaluated, the China Meteorological Forcing Dataset (CMFD) and WATCH Forcing Data methodology applied to ERA5 (WFDE5/CRU+GPCC) are identified as the most accurate products, supported by both statistical and hydrological comparisons. This consistency in statistical and hydrological comparisons suggests the potential applicability of the hydrological comparison method using the AHMS in ungagged catchments, even in the presence of significant anthropogenic impacts. Furthermore, the calibration of the hydrological model significantly impacts the model’s response to precipitation, effectively compensating for deficiencies in rainfall data within certain limits. This study highlights accurate representation of extreme rainfall events in precipitation products has a significant impact on calibrated soil parameters and is particularly important in hydrological modelling. It enhances our understanding of the reliability of hydrological simulations and provides valuable insights for the assessment of precipitation reanalysis products in large arid and semiarid basins affected by human activities.

Evaluation of 12 precipitation products and comparison of 8 multi-model averaging methods for estimating precipitation in the Qilian Mountains, Northwest China

Mountains are the water towers of the world, so it is critical to obtain accurate precipitation data for mountainous areas. Due to the complex topography of high mountainous areas, precipitation ground stations are sparse and unevenly distributed in such areas, so precipitation products such as remote sensing and reanalysis products are used to obtain gridded precipitation data for these areas. However, no single precipitation product performs best in all areas of mountainous regions. Therefore, this study first evaluated the performance of 12 precipitation products in estimating precipitation in the Qilian Mountains at the station scale and sub-basin scale, and then compared the performance of precipitation estimates for the Qilian Mountains generated by 8 multi-model averaging methods. The evaluation results for 29 meteorological stations in the Qilian Mountains showed that the China Meteorological Forcing Dataset product was the best-performing precipitation product, while the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System-Climate Data Record product was the worst-performing precipitation product. The evaluation results for 18 sub-basins showed that at these sub-basins, the WorldClim was the best-performing precipitation product, while the High Asia Refined analysis was the worst-performing precipitation product. Thus, station-scale evaluations may not necessarily be applicable to the basin scale. Multi-model averaging methods effectively improved the accuracy of precipitation estimates both at station scale and at sub-basin scale. The Granger-Ramanathan variant C was the best multi-model averaging method for estimating precipitation at station scale. As the Granger-Ramanathan methods allow negative weights, they are not recommended to interpolate the Granger-Ramanathan weight values of stations to grids. The Bayesian model averaging (BMA) was found to be the most suitable multi-model averaging method for estimating precipitation in the Qilian Mountains by interpolation of weight values of stations to grids. The precipitation estimates generated by BMA show that the mean annual precipitation in the Qilian Mountains from 2001 to 2018 was approximately 336.1 mm, and the annual precipitation during this period increased linearly by 2.4 mm per year.

Evaluation of multiple gridded snowfall datasets using gauge observations over high mountain Asia

Snowfall is an important component of the High Mountain Asia (HMA) cryosphere; its changes affect the regional energy and water balance. Snowfall data from numerous gridded products provide the basis for conducting climate-change studies; however, the applicability of these datasets in HMA has not been assessed comprehensively in terms of precipitation. This study comprehensively evaluates the applicability of the following widely used gridded datasets in HMA for the period 1991–2014: fifth generation of European ReAnalysis (ERA5), Global Land Data Assimilation System (GLDAS), High Asia Refined analysis (HAR), Multi-Source Weather (MSWX), National Centers for Environmental Prediction (NCEP), and China Meteorological Forcing Dataset (CMFD). The accuracy features of each dataset were evaluated based on observations from 148 gauges at various temporal scales (i.e., annual, monthly, and daily), elevations, sub-regions, and snowfall intensities. The results show that CMFD performs better at multiple temporal scales (i.e., annual, cool season, monthly, and daily) than other snowfall data. In addition, the CMFD accuracy has good stability, even at different elevations. CMFD is advantageous regarding interpreting the frequency and amount of different levels of (even extreme) snowfall events. The slightly inferior MSWX also performs well at different timescales, spatial scales, and elevations, and constitutes an attractive alternative to CMFD. HAR captures better light and moderate snowfall, and is the most accurate of the three sets of gridded products that contain direct snowfall data. This study constitutes an important reference for selecting suitable snowfall datasets for future scientific research.

Evaluation of ERA5 Reanalysis Precipitation Data in the Yarlung Zangbo River Basin of the Tibetan Plateau

Abstract Atmospheric simulation-based gridded precipitation datasets have been widely used in hydrological and land surface modeling, but may contain larger uncertainties in mountainous regions. This study compared the performance of the fifth European Centre for Medium-Range Weather Forecasts reanalysis (ERA5) precipitation data with two fused precipitation datasets [China Meteorological Administration Land Data Assimilation System version 2.0 (CLDAS2.0) and China Meteorological Forcing Dataset (CMFD)] in the Yarlung Zangbo River basin (YZRB), which has a complex terrain and climate. Compared to in situ observations, ERA5 could capture the spatial–temporal pattern of precipitation but showed high precipitation, especially in the downstream region (lower Nuxia discharge station). In terms of the correlation coefficient, the overall performance of the ERA5 data was slightly worse than that for CMFD data at both the monthly and yearly scales, and was comparable to that of the CLDAS2.0 data. Given that the spatial mismatch between the gridded and in situ data may influence the evaluation, we also employed the water balance method to constrain basinwide precipitation amounts. We found that CLDAS2.0 and CMFD precipitation data tended to cause long-term water imbalance, and ERA5, with a much larger multiyear average annual precipitation, could better close the water budget. Further analysis showed that the differences in multiyear average annual precipitation between ERA5 and in situ observations were closely related to the slope and standard deviation of the subgrid-scale orography, indicating the substantial influence of subgrid topography on precipitation simulation. These findings highlight that ERA5 could be a potential reference dataset for hydrological modeling of the YZRB.

Evaluation of MSWX Bias-Corrected Meteorological Forcing Datasets in China

Near-surface meteorological forcing (NSMF) datasets, mixed observations, and model forecasts are widely used in global climate change and sustainable development studies. For practical purposes, it is important to evaluate NSMF datasets, especially those released latest, and determine their strengths and limitations. In this study, we evaluate the performance of Multi-Source Weather (MSWX) in China over the period of 1979–2016. For comparison, ECMWF Reanalysis version 5 (ERA5), China Meteorological Forcing Dataset (CMFD) and Princeton Global Forcing (PGF) dataset are also evaluated to determine the strengths and weaknesses of MSWX. The following variables are compared with observations over 2400 stations: 2 m air temperature (T2m), 2 m daily maximum air temperature (Tmax), 2 m daily minimum air temperature (Tmin), precipitation (P), and 10 m wind speed (V10). The evaluation is conducted in terms of climatology, inter-annual variations and seasonal cycles. Results show that MSWX reasonably reproduces the spatial pattern of T2m with root-mean-square errors (RMSEs) below 1.12 °C and spatial correlations above 0.97, but underestimates Tmax and overestimates Tmin, with biases ranging from −2.0 °C to 2.0 °C, especially over the North China and Northeast China. Compared with ERA5 and PGF, MSWX can better simulate the inter-annual variations of surface air temperature with high spatial correlations (>0.97) but shows higher RMSEs than PGF. For precipitation, MSWX accurately captures the primary features of precipitation, including significant characteristics or patterns of the precipitation climatology and inter-annual variation. Its inter-annual variation shows low RMSEs ranging from 0.55 mm/day to 0.8 mm/day, compared to ERA5 and PGF. However, regions with abundant precipitation exhibit higher biases. Because the biased Global Wind Atlas (GWA3.1) is used for the wind bias correction of MSWX, MSWX significantly overestimates the annual mean wind speed, but it is consistently well-correlated with observations, with RMSEs less than 1.5 m/s and spatial correlations greater than 0.6 over the period of 1979–2016. This study reveals both the advantages and disadvantages of MSWX, and indicates the need for research into climate change and sustainable development in East Asia.

Climate Change and Hydrological Response in the Ranwu Lake Basin of Southeastern Tibet Plateau

It is of great practical significance to accurately distinguish the different water supply sources of rivers and lakes under climate change for regional water resources utilization. This study examines the impact of climate change on the hydrological processes of the Ranwu Lake basin in the southeastern Tibet Plateau. The authors used China Meteorological Forcing Dataset (CMFD) historical data, CanESM5′s future climate predictor, and the SPHY model to analyze trends in temperature, precipitation, and water supply sources in the basin. The study found that warming in the basin was higher than that in the Tibet Plateau, with high-altitude areas and winter showing more significant warming. From 1998 to 2018, precipitation in the basin showed a trend of fluctuation and decline. The study also found that glacial meltwater accounted for the majority of total runoff in the basin (54.13%), while snow meltwater, rainfall, and baseflow accounted for about 22.98%, 11.84%, and 11.06%, respectively, on average in recent years. The total runoff in the Ranwu Lake Basin will continue to decrease due to the accelerating retreat of glaciers, with the hydrological process transforming from being dominated by glacier processes to rain–snow processes. The study also predicts that three-quarters of glaciers in the basin will vanish within the next forty years, and by 2100, only around 20% of glaciers will remain.

The possible role of fused precipitation data in detection of the spatiotemporal pattern of rainfall erosivity over the Tibetan Plateau, China

As a typical fragile ecological plateau area, the risk of water erosion on the Tibetan Plateau (TP) in China continues to increase with climate change. Rainfall erosivity is one of the most widely used indicators to assess the potential impact of rainfall events on water erosion. However, limited by the scarcity of historical in situ precipitation observations with sufficient spatiotemporal resolution, the estimates of rainfall erosivity over the TP have much larger biases than those of other regions in China. To accurately investigate the spatiotemporal evolution of rainfall erosivity, empirical models were first established to estimate monthly rainfall erosivity using 1-minute in situ precipitation observations from 1711 meteorological stations on the TP. The independent assessment showed that the correlation correction values between the observed and estimated monthly values were greater than 0.81 for all months. The annual rainfall erosivity data were then produced with a 0.1° spatial resolution for the 1979–2018 period based on the China Meteorological Forcing Dataset (CMFD) precipitation data using newly established estimation models. Our results show that the CMFD-based estimates successfully captured the decreasing spatial pattern of the multiyear average annual rainfall erosivity from the southeast to northwest of the TP. In addition, the CMFD-based annual rainfall erosivity had a good linear relationship with the observed values but with a certain overestimation. Therefore, standardized annual rainfall erosivity values were used to detect the changes in rainfall erosivity. For most regions on the TP, annual rainfall erosivity values have exhibited significant increasing trends over the last 40 years. This study provides a theoretical basis and reference for controlling water erosion and climate adaptation on the TP.

Analysis of the Applicability of Multisource Meteorological Precipitation Data in the Yunnan-Kweichow-Plateau Region at Multiple Scales

Multisource meteorological precipitation products are an important way to make up for a lack of observation sites or a lack of precipitation data in areas with a complex topography. They have important value for local industrial, agricultural, and ecological water use calculations, as well as for water resource evaluation and management. The Yunnan-Kweichow Plateau is located in southwest China and has a relatively backward economy and few meteorological stations. At the same time, the terrain is dominated by mountain valleys, precipitation is greatly affected by the terrain, and meteorological data are lacking, making the calculation of local water resources difficult. In this study, the applicability of the 3-hourly merged high-quality/IR estimates (3B42) of the Tropical Rainfall Measuring Mission (TRMM), China Meteorological Forcing Dataset (CMFD), and China Meteorological Assimilation Driving Datasets (CMADS) in the Yunnan-Kweichow Plateau was analyzed using multiple evaluation indicators of different temporal scales and precipitation intensity levels as well as the spatial distribution of the indicators based on measured daily precipitation data from 59 national meteorological basic stations in the study area in 2008–2018. The results showed that (1) the three products had performed well and could be applied to the calculation of local water resources with CMFD performing the best; (2) the performance of precipitation products was slightly worse on the daily scale, and the overall performance of the yearly, quarterly, and monthly scales was better; (3) good results were achieved in most regions, but there were also some regions with prominent overestimation and underestimation; (4) the three precipitation products had the highest probabilities of detection and the lowest false alarm rates for no rain and light rain, and the probability of detection gradually decreased with an increase in the precipitation intensity; and (5) the mean absolute error of precipitation products in rainy months is large, so the accuracy of products in the calculation of heavy rain and flood will be limited.

Calibration and Evaluation of the WRF-Hydro Model in Simulating the Streamflow over the Arid Regions of Northwest China: A Case Study in Kaidu River Basin

In this study, the hydrological system of the Weather Research and Forecasting model (WRF-Hydro) is applied to simulate the streamflow at the Kaidu River Basin, which is vital to the ecological system in the lower reaches of the Tarim River in Northwest China. The offline WRF-Hydro model is coupled with the Noah multi-parameterization land surface model (Noah-MP) and is forced by the China Meteorological Forcing Dataset (CMFD), with the grid spacing of the hydrological routing modules being 250 m. A 3-year period (1983–1985) is used for calibration and a 17-year period (1986–2002) for the evaluation. Several key parameters of WRF-Hydro and four Noah-MP parameterization options are calibrated, and the performance of WRF-Hydro with the optimized model setting is evaluated using the daily streamflow observations. The results indicate that WRF-Hydro can reproduce the observed streamflow reasonably, with underestimation of the streamflow peaks. The simulated streamflow is sensitive to the parameters of bexp, dksat, smcmax, REFKDT, slope, OVROUGHRTAC and mann in the Kaidu River Basin. At the same time, the parameterization options of Noah-MP also have a large influence on the streamflow simulation. The WRF-Hydro model with optimized model settings can achieve correlation coefficient (CC) and Nash efficiency coefficient (NSE) statistical scores of 0.78 and 0.61, respectively, for the calibration period. Meanwhile, for the evaluation period, the scores are 0.7 and 0.50, respectively. This study indicates the importance of applying the physical-based WRF-Hydro model over Northwest China and provides a reference for the nearby regions.

Performance Evaluation and Improvement of CMFD's Precipitation Products Over Shanghai City, China

AbstractPrecipitation products with high resolution for urban areas remain an important and challenging issue. The main purposes of this study are to evaluate the accuracy of the China Meteorological Forcing Data Set (CMFD) in the Shanghai megacity and to improve the accuracy of the CMFD precipitation. With the rain gauge observations for the 2013–2018 period, we evaluate the error and detection ability of CMFD at the sub‐daily, daily, and monthly scales, and examine the spatial variation of CMFD performance over the region. The Geographic Differential Analysis (GDA) method is used to calibrate the CMFD precipitation data set. The results show that the performance of CMFD precipitation varies from sub‐daily to monthly time scales with better accuracy at the monthly scale in the study region. The CMFD can capture the spatial variability over the region with less error in the north and outliers in the center. The GDA‐based calibration framework can significantly improve the capability of CMFD products, especially at the sub‐daily and daily scales. The calibrated CMFD can provide reliable precipitation estimates and capture more extreme precipitation events. The improved CMFD products can provide a key basis for applications related to hydrological and meteorological hazard monitoring and assessment in the Shanghai area. The overall framework is scalable and could be readily generalized to other regions, and the calibrated product can be used in the research of urban natural disaster risks and other studies.

Rethinking spatiotemporal variations in air temperature over the Qilian Mountains, Western China, from 1979 to 2018

The Qilian Mountains region, located on the northeastern Tibetan Plateau, is a region in western China that is sensitive to climate change and serves as an important ecological security barrier. Previous studies mostly used meteorological stations to study climate change in the Qilian Mountains; however, due to the known distribution limitations (scarcity and inhomogeneity) of meteorological stations in mountainous areas, relevant results remain inconclusive. To address this issue, we used 30 meteorological stations in and around the Qilian Mountains and the China meteorological forcing dataset (CMFD) with a spatial resolution of 0.1° × 0.1° to comprehensively analyse the spatiotemporal distribution of and variation in the air temperature over the Qilian Mountains, with a particular focus on elevation-dependent warming (EDW); then, land surface albedo data from the ERA5-Land and MODIS products were employed to interpret the EDW. The results showed that the annual mean lapse rate in the Qilian Mountains is −0.56 °C/100 m. From 1979 to 2018, the air temperature over the Qilian Mountains experienced a significant upwards trend with an annual mean rate of 0.39 °C/decade, the maximum rate occurring in December–February (DJF, 0.56 °C/decade) and the minimum rate occurring in June–August (JJA, 0.28 °C/decade). Spatially, significant warming was found in most areas of the Qilian Mountains, and a slight cooling trend was found in the north and southeast. Unlike other typical areas, the Qilian Mountains showed reversed EDW during 1979–2018, i.e., the increasing trends in air temperature decreased with elevation, which was mainly caused by the reversal in EDW over the 1999–2008 period and could be largely explained by the change in land surface albedo.

The first high spatial resolution multi-scale daily SPI and SPEI raster dataset for drought monitoring and evaluating over China from 1979 to 2018

ABSTRACT Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI), traditionally derived at a monthly scale, are widely used drought indices. To overcome temporal-resolution limitations, we have previously developed and published a well-validated daily SPI/SPEI in situ dataset. Although having a high temporal resolution, this in situ dataset presents low spatial resolution due to the scarcity of stations. Therefore, based on the China Meteorological Forcing Dataset, which is composed of data from more than 1,000 ground-based observation sites and multiple remote sensing grid meteorological dataset, we present the first high spatiotemporal-resolution daily SPI/SPEI raster datasets over China. It spans from 1979 to 2018, with a spatial resolution of 0.1° × 0.1°, a temporal resolution of 1-day, and the timescales of 30-, 90-, and 360-days. Results show that the spatial distributions of drought event characteristics detected by the daily SPI/SPEI are consistent with the monthly SPI/SPEI. The correlation between the daily value of the 12-month scale and the monthly value of SPI/SPEI is the strongest, with linear correlation, Nash-Sutcliffe coefficient, and normalized root mean square error of 0.98, 0.97, and 0.04, respectively. The daily SPI/SPEI is shown to be more sensitive to flash drought than the monthly SPI/SPEI. Our improved SPI/SPEI shows high accuracy and credibility, presenting enhanced results when compared to the monthly SPI/SPEI. The total data volume is up to 150 GB, compressed to 91 GB in Network Common Data Form (NetCDF). It can be available from Figshare (https://doi.org/10.6084/m9.figshare.c.5823533) and ScienceDB (https://doi.org/10.57760/sciencedb.j00076.00103).

Mapping reveals contrasting change patterns of rain-on-snow events in China during 2001–2018

Rain-on-snow (ROS) events can accelerate snow melting, which in turn leads to disasters such as floods, avalanches, and landslides. However, little is known about the characteristics of and changes in ROS events in China. In this study, the temporal and spatial variations of ROS days in China from 2001 to 2018 were investigated using the improved Moderate Resolution Imaging Spectroradiometer snow cover data, and China Meteorological Forcing Dataset. The results suggested that ROS days in China mainly occurred from November to June and were mainly distributed in northern Xinjiang (6.1 days/year), Qinghai-Tibet Plateau (2.8 days/year), northeast China (3.5 days/year), and east China (1.1 days/year). ROS days in each region have different seasonal distribution patterns. In the past 18 years, ROS days have shown a trend of increasing at higher latitudes and decreasing at lower latitudes in China, especially in northern Xinjiang and the Qinghai-Tibet Plateau. ROS days are decreasing below 3000 m in northern Xinjiang (-0.16 days/year) and below 4000 m in the Qinghai-Tibet Plateau (-0.21 days/year), which is explained by the decrease of snow days and rain days. ROS days are increasing at 3000–4000 m altitudes (0.15 days/year) in the Tianshan Mountains and 4000–5500 m altitudes (0.06 days/year) in the eastern Tibetan Mountains, mainly explained by more snowfall converted to rainfall. The ROS days along the Greater Khingan Mountains in Northeast China showed a decreasing trend (-0.12 days/year), and along the Changbai Mountains showed an increasing trend (0.07 days/year). The variation of snow cover days is the dominant factor. East China as a whole shows a decreasing trend (-0.1 days/year), explained by a decrease in snow cover time. In China, ROS days at low altitudes are negatively correlated with annual average temperature, and positively at high altitudes. The changes of ROS were also correlated with Arctic Oscillation and North Atlantic Oscillation. This study reveals the response of ROS to climate change in China, and provides an important reference for coping with the related disasters and changes caused by ROS events.

Revisiting the variations of precipitation and water vapour budget over the Tibetan Plateau

Owing to the scarcity of observation data in the western Tibetan Plateau (TP), the knowledge of precipitation changes over the entire plateau based only on the limited data in eastern TP is not reliable. Therefore, the alternative high-resolution precipitation data of the China Meteorological Forcing Dataset (CMFD) are used for the comprehensive analysis of precipitation changes over the whole TP (including western and northern TP) to fill in the lack of understanding of precipitation in the western TP. Compared with observations, CMFD can broadly capture the spatial distributions and identify the temporal variabilities of precipitation over the TP. Results with CMFD data suggested that the annual precipitation over the whole TP did not show a uniform humidification trend in 1979–2018 and featured wetting and drying trends in the northern (NTP) and southern TP (STP), respectively. Additionally, the four seasonal regimes of precipitation over the northern TP (NTP, including most areas of western TP) all experienced a noticeable interdecadal shift around the late 1990s, followed by above-normal precipitation. Except for spring, the seasonal precipitation over the southern TP (STP) showed interannual variations. Spring precipitation over the STP has undergone moistening since the late 1990s, which was consistent with that over the NTP. Four different reanalysis datasets, namely JRA55, MERRA2, ERA5 and CRA40, were used to compare the water vapour budget of each boundary over the TP. The increase in spring precipitation over the NTP and STP was found to be related to the decrease in water vapour outflow from the north boundary. The interdecadal increase in summer precipitation over the NTP was mainly due to the reduction of outflow from the east boundary. Finally, the increase in autumn precipitation was related to the increase in inflow from the west boundary.