Precipitation in China increased to 661.09 mm in 2024 from 608.68 mm in 2023. This dataset includes a chart with historical data for China Average Precipitation.

Important Notice: These old dataset versions (V2.0.x) have been superseded. A new version, CHM_PRE V2.1, is now available and is recommended for all users (https://doi.org/10.5281/zenodo.14632156). The updated version (V2.1) extends the data coverage to 2024 and incorporates adjusted precipitation values for the southern foothills of the Himalayas.

The CHM_PRE V2 dataset is a new high-precision, long-term, daily gridded precipitation dataset for mainland China. The long-term daily observation from 3,476 gauges and incorporated 11 related precipitation variables were utilized to characterize the correlations of precipitation. Then, the dataset was developed by employing an improved inverse distance weighting method combined with the machine learning-based light gradient boosting machine (LGBM) algorithm. CHM_PRE V2 demonstrates strong spatiotemporal consistency with existing gridded precipitation datasets, including CHM_PRE V1, GSMaP, IMERG, PERSIANN-CDR, and GLDAS. Validation against 63,397 high-density gauges confirms its high accuracy in both precipitation values and events. The dataset achieves a mean absolute error of 1.48 mm/day and a Kling-Gupta efficiency coefficient of 0.88. In terms of event detection capability, CHM_PRE V2 achieves a Heidke skill score of 0.68 and a false alarm ratio of 0.24. Overall, CHM_PRE V2 significantly enhances precipitation measurement accuracy and reduces the overestimation of precipitation events, providing a reliable foundation for hydrological modeling and climate assessments. The CHM_PRE V2 dataset provides daily precipitation data with a resolution of 0.1°, covering the entire mainland China (18°N–54°N, 72°E–136°E). This dataset covers the period of 1960–2023, and will be continuously updated annually. The daily precipitation data is provided in NetCDF format, and for the convenience of users, we also offer annual and monthly total precipitation data in both NetCDF and GeoTIFF formats.

This dataset includes the monthly mean temperature data with 0.0083333 arc degree (~1km) for China from Jan 1901 to Dec 2023. The data form belongs to NETCDF, namely .nc file. The unit of the data is 0.1 ℃. The dataset was spatially downscaled from CRU TS v4.02 with WorldClim datasets based on Delta downscaling method. The dataset was evaluated by 496 national weather stations across China, and the evaluation indicated that the downscaled dataset is reliable for the investigations related to climate change across China. The dataset covers the main land area of China, including Hong Kong, Macao and Taiwan regions, and excluding islands and reefs in South China Sea. WGS84 is recommended for data coordinate system.

The Chinese Mainland Annual Rainfall Erosivity Fusion Gridded Dataset is a long-term, high-resolution rainfall erosivity (RE) dataset covering 38 years (1983–2020). It aims to provide fundamental data support for studies on soil erosion, hydrological modeling, and ecological and environmental management. The dataset spans the period from 1983 to 2020, with a spatial resolution of 0.25° × 0.25°.During dataset construction, observational data from more than 545 meteorological stations across China were used as reference data. The Extra Trees Regression (ETR) machine learning model was employed to fuse annual rainfall erosivity estimates derived from five commonly used gridded daily precipitation datasets. The estimation of rainfall erosivity followed the method proposed by Xie et al. (2016):Xie, Y., Yin, S.-q., Liu, B.-y., Nearing, M.A., Zhao, Y. (2016). Models for estimating daily rainfall erosivity in China. Journal of Hydrology, 535, 547–558. https://doi.org/10.1016/j.jhydrol.2016.02.020The five precipitation datasets used for fusion include:Satellite-based precipitation products: CHIRPS;Gauge-interpolated precipitation products: CN05.1, CHM, CPC;Reanalysis precipitation product: ERA5.The annual rainfall erosivity data were generated by fusing CHIRPS, CPC, CN05.1, CHM, and ERA5 datasets using the ETR model. Each file is named “RE_yyyy”, where yyyy denotes the corresponding year (1983–2020).The dataset provides rainfall erosivity values for three rainfall intensity categories:Moderate RE: calculated from daily rainfall between 10 and 24.9 mm;Large RE: calculated from daily rainfall between 25 and 49.9 mm;Heavy RE: calculated from daily rainfall greater than or equal to 50 mm.This dataset integrates the strengths of multiple precipitation products—combining the spatial continuity of satellite data, the local accuracy of ground-based observations, and the physical consistency of reanalysis data. It provides high-precision rainfall erosivity information for studies of soil erosion across regions with diverse climatic and topographic conditions in China.

Accurate long-term temperature and precipitation estimates at high spatial and temporal resolutions are vital for a wide variety of climatological studies. We have produced a new, publicly available, daily, gridded maximum temperature, minimum temperature, and precipitation dataset for China with a high spatial resolution of 1 km and over a long-term period (1961 to 2019). It has been named the HRLT. The daily gridded data were interpolated using comprehensive statistical analyses, which included machine learning, the generalized additive model, and thin plate splines. It is based on the 0.5° × 0.5° grid dataset from the China Meteorological Administration, together with covariates for elevation, aspect, slope, topographic wetness index, latitude, and longitude. The accuracy of the HRLT daily dataset was assessed using observation data from meteorological stations. The maximum and minimum temperature estimates were more accurate than the precipitation estimates. For maximum temperature, the mean absolute error (MAE), root mean square error (RMSE), Pearson's correlation coefficient (Cor), coefficient of determination after adjustment (R²), and Nash-Sutcliffe modeling efficiency (NSE) were 1.07 °C, 1.62 °C 0.99, 0.98, and 0.98, respectively. For minimum temperature, the MAE, RMSE, Cor, R², and NSE were 1.08°C, 1.53 °C, 0.99, 0.99, and 0.99, respectively. For precipitation, the MAE, RMSE, Cor, R², and NSE were 1.30 mm, 4.78 mm, 0.84, 0.71, and 0.70, respectively. The accuracy of the HRLT was compared to those of the other three existing datasets and its accuracy was either greater than the others, especially for precipitation, or comparable in accuracy, but with higher spatial resolution and over a longer time period. In summary, the HRLT dataset, which has a high spatial resolution, covers a longer period of time and has reliable accuracy, is suitable for future environmental analyses, especially the effects of extreme weather.

This archived Paleoclimatology Study is available from the NOAA National Centers for Environmental Information (NCEI), under the World Data Service (WDS) for Paleoclimatology. The associated NCEI study type is Climate Reconstruction. The data include parameters of climate reconstructions|paleoclimatic modeling with a geographic location of China, Eastern Asia. The time period coverage is from 21995 to -63 in calendar years before present (BP). See metadata information for parameter and study location details. Please cite this study when using the data.

The annual datasets as "teaser data", can make it much more easy for potential users to have a brief look at daily datasets. The datasets are annual average temperature (maximum temperature and minimum temperature) and annual accumulated precipitation with 1 km spatial resolution over 1961-2019, and are calculated from the daily, gridded maximum temperature, minimum temperature, and precipitation dataset for China (https://doi.org/10.1594/PANGAEA.941329).

This dataset provides 30-year averaged climate data for both historical and future periods, with a spatial resolution of 0.01° × 0.01°. Historical data (1991–2020) are based on the China Surface Climate Standard Dataset and were interpolated using ANUSPLIN software. Future climate data are derived from CMIP6 simulations, bias-corrected using the Delta downscaling method. The dataset includes 10 models (9 Global Climate Models, namely, GCMs, and 1 ensemble model), 3 scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5), and 3 future periods (2021–2040, 2041–2070, 2071–2100). For each period (or scenario), 28 climate variables are provided, including: 5 monthly basic climate variables (mean temperature, maximum temperature, minimum temperature, precipitation, and percentage of sunshine), and 23 bioclimatic variables based on the basic variables (for details, see the dataset documentation file).The data quality was strictly evaluated. The ANUSPLIN interpolated historical data showed a strong correlation with observations (all correlation coefficients above 0.91). The historical interpolations generated by the ANUSPLIIN software showed a good fit (above 0.91) with observations. The bias correction improved the accuracy of most GCM original simulations, reducing the bias by 0.69%–58.63%. This dataset aims to provide high-resolution, bias-corrected long-term historical and future climate data for climate and ecological research. All computations were performed using R, and the corresponding code can be found in the dataset folder: “Code”.All data are provided in GeoTIFF (.tif) format, where each file for the basic climate variables contains 12 bands, representing monthly data in ascending order (e.g., Band 1 corresponds to January). To facilitate data storage, all files are provided in compressed archives, following a consistent naming convention:(1) Historical data: China_Variable_1km_1991–2020.tifWhere, Variable represents the abbreviation of the 28 climate variables.Example: China_pr_1km_1991–2020.tif.(2) Future data: China_Variable_Model_VariantLabel_1km_StartYear-EndYear_Scenario.tifWhere, Variable is the 28 climate variables; Model is the GCM name; VariantLabel is r1i1p1f1 in this study; StartYear-EndYear is the future period; Scenario is the SSP climate scenarioExample: China_tasmin_MRI-ESM2-0_r1i1p1f1_1km_2071–2100_SSP585.tif.

Gridded climatic datasets with fine spatial resolution can potentially be used to depict the climatic characteristics across the complex topography of China. In this study we collected records of monthly temperature at 1153 stations and precipitation at 1202 stations in China and neighboring countries to construct a monthly climate dataset in China with a 0.025° resolution (~2.5 km). The dataset, named LZU0025, was designed by Lanzhou University and used a partial thin plate smoothing method embedded in the ANUSPLIN software. The accuracy of LZU0025 was evaluated based on three aspects: (1) Diagnostic statistics from the surface fitting model during 1951–2011. The results indicate a low mean square root of generalized cross validation (RTGCV) for the monthly air temperature surface (1.06 °C) and monthly precipitation surface (1.97 mm1/2). (2) Error statistics of comparisons between interpolated monthly LZU0025 with the withholding of climatic data from 265 stations during 1951–2011. The results show that the predicted values closely tracked the real true values with values of mean absolute error (MAE) of 0.59 °C and 70.5 mm, and standard deviation of the mean error (STD) of 1.27 °C and 122.6 mm. In addition, the monthly STDs exhibited a consistent pattern of variation with RTGCV. (3) Comparison with other datasets. This was done in two ways. The first was via comparison of standard deviation, mean and time trend derived from all datasets to a reference dataset released by the China Meteorological Administration (CMA), using Taylor diagrams. The second was to compare LZU0025 with the station dataset in the Tibetan Plateau. Taylor diagrams show that the standard deviation, mean and time trend derived from LZU had a higher correlation with that produced by the CMA, and the centered normalized root-mean-square difference for this index derived from LZU and CMA was lower. LZU0025 had high correlation with the Coordinated Energy and Water Cycle Observation Project (CEOP) - Asian Monsoon Project, (CAMP) Tibet surface meteorology station dataset for air temperature, despite a non-significant correlation for precipitation at a few stations. Based on this comprehensive analysis, we conclude that LZU0025 is a reliable dataset. LZU0025, which has a fine resolution, can be used to identify a greater number of climate types, such as tundra and subpolar continental, along the Himalayan Mountain. We anticipate that LZU0025 can be used for the monitoring of regional climate change and precision agriculture modulation under global climate change.

The new daily gridded precipitation dataset over the Chinese mainland (CHM_PRE) is a long-term precipitation dataset with fine spatiotemporal resolution developed to facilitate the advancement of drought monitoring, flood forecasting, and hydrological modeling. Its record starts from 1961 and keeps updating yearly (currently up to 2022) with spatial resolutions of 0.5° × 0.5°, 0.25° × 0.25°, and 0.1° × 0.1°. Using daily observations from 2,839 gauges across China and nearby regions, the dataset was made through the daily climatology field combined with the precipitation ratio field algorithm. The two interpolation fields were adjusted based on monthly precipitation constraint and topographic characteristic correction.

Global attributes:
· Variable = daily precipitation (pre) · Variable = number of stations in each grid every year (station_number) · Date format = NetCDF · Temporal Range = 1961-01-01 to 2022-12-31 · Spatial extent = 18°N–54°N, 72°E–136°E · Units = mm/day · Missing value = -99.9

The CHM_PRE V2 dataset is a new high-precision, long-term, daily gridded precipitation dataset for mainland China. The long-term daily observation from 3,476 gauges and incorporated 11 related precipitation variables were utilized to characterize the correlations of precipitation. Then, the dataset was developed by employing an improved inverse distance weighting method combined with the machine learning-based light gradient boosting machine (LGBM) algorithm. CHM_PRE V2 demonstrates strong spatiotemporal consistency with existing gridded precipitation datasets, including CHM_PRE V1, GSMaP, IMERG, PERSIANN-CDR, and GLDAS. Validation against 63,397 high-density gauges confirms its high accuracy in both precipitation values and events. The dataset achieves a mean absolute error of 1.48 mm/day and a Kling-Gupta efficiency coefficient of 0.88. In terms of event detection capability, CHM_PRE V2 achieves a Heidke skill score of 0.68 and a false alarm ratio of 0.24. Overall, CHM_PRE V2 significantly enhances precipitation measurement accuracy and reduces the overestimation of precipitation events, providing a reliable foundation for hydrological modeling and climate assessments. The CHM_PRE V2 dataset provides daily precipitation data with a resolution of 0.1°, covering the entire mainland China (18°N–54°N, 72°E–136°E). This dataset covers the period of 1960–2023, and will be continuously updated annually. The daily precipitation data is provided in NetCDF format, and for the convenience of users, we also offer annual and monthly total precipitation data in both NetCDF and GeoTIFF formats.

The monthly air temperature in 1153 stations and precipitation in 1202 stations in China and neighboring countries were collected to construct a monthly climate dataset in China on 0.025 ° resolution (approximately 2.5 km) named LZU0025 dataset designed by Lanzhou University (LZU), using a partial thin plate smoothing method embedded in the ANUSPLIN software. The accuracy of the LZU0025 was evaluated from analyzing three aspects: 1) Diagnostic statistics from surface fitting model in the period of 1951-2011, and results show low mean square root of generalized cross validation (RTGCV) for monthly air temperature surface (1.1 °C) and monthly precipitation surface (2 mm1/2) which interpolated the square root of itself. This indicate exact surface fitting models. 2) Error statistics based on 265 withheld stations data in the period of 1951-2011, and results show that predicted values closely tracked true values with mean absolute error (MAE) of 0.6 °C and 4 mm and standard deviation of mean error (STD) of 1.3 °C and 5 mm, and monthly STDs presented consistent change with RTGCV varying. 3) Comparisons to other datasets through two ways, one was to compare three indices namely the standard deviation, mean and time trend derived from all datasets to referenced dataset released by the China Meteorological Administration (CMA) in the Taylor diagrams, the other was to compare LZU0025 to the Camp Tibet dataset on mountainous remote area. Taylor diagrams displayed the standard deviation derived from LZU had higher correlation with that induced from CMA (Pearson correlation R=0.76 for air temperature case and R=0.96 for precipitation case). The standard deviation for this index derived from LZU was more close to that induced from CMA, and the centered normalized root-mean-square difference for this index derived from LZU and CMA was lower. The same superior performance of LZU were found in comparing indices of the mean and time trend derived from LZU and those induced from other datasets. LZU0025 had high correlation with the Camp dataset for air temperature despite of insignificant correlation for precipitation in few stations. Based on above comprehensive analyses, LZU0025 was concluded as the reliable dataset.

A data base containing meteorological observations from the People's Republic of China (PRC) is described. These data were compiled in accordance with a joint research agreement signed by the U.S. Department of Energy and the PRC Chinese Academy of Sciences (CAS) on August 19, 1987. CAS's Institute of Atmospheric Physics (Beijing, PRC) has provided records from 296 stations, organized into five data sets: (1) a 60-station data set containing monthly measurements of barometric pressure, surface air temperature, precipitation amount, relative humidity, sunshine duration, cloud amount, wind direction and speed, and number of days with snow cover; (2) a 205-station data set containing monthly mean temperatures and monthly precipitation totals; (3) a 40-station subset of the 205-station data set containing monthly mean maximum and minimum temperatures and monthly extreme maximum and minimum temperatures; (4) a 180-station data set containing daily precipitation totals; and (5) a 147-station data set containing 10-day precipitation totals. Sixteen stations from these data sets (13 from the 60-station set and 3 from the 205-station set) have temperature and/or precipitation records that begin prior to 1900, whereas the remaining stations began observing in the early to mid-1900s. Records from most stations extend through 1988. (Note: Users interested in the TR055 60-station data set should acquire expanded and updated data from CDIAC's NDP-039, Two Long-Term Instrumental Climatic Data Bases of the People's Republic of China)For access to the data files, click this link to the CDIAC data transition website: http://cdiac.ess-dive.lbl.gov/ndps/tr055.htmlThis dataset was transferred from the CDIAC Archive and published on ESS-DIVE in 2018 under the project title "Carbon Dioxide Information Analysis Center (CDIAC), Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (USA); Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China". In 2023, the project title was updated to "Carbon Dioxide Information Analysis Center (CDIAC); Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)" to enable consistent management of all datasets previously hosted by the CDIAC Archive that are now published on ESS-DIVE.

These datasets fill the data gap between GRACE and GRACE-FO, they contain CSR RL06 Mascon and JPL RL06 Mascon. They take China as the study area, and the dataset includes "Decimal_time”, "lat”, "lon”, "time”, "time_bounds”, "TWSA_REC" and "Uncertainty" 7 parameters in total. Among them, "Decimal_time” corresponds to decimal time. There are 191 months from April 2002 to December 2019 (163 months for GRACE data, 17 months for GRACE-FO data, and 11 months for the gap between GRACE and GRACE-FO. We have not filled the missing data of individual months between GRACE or GRACE-FO data). "lat" corresponds to the latitude range of the data; "lon" corresponds to the longitude range of the data; "time" corresponds to the cumulative day of the data from January 1, 2002. And "time_bounds" corresponding to the cumulative day at the start date and end date of each month. “TWSA_REC" represents the monthly terrestrial water storage anomalies from April 2002 to December 2019 in China; "Uncertainty" is the uncertainty between the data and CSR RL06 Mascon products. We use GRACE satellite data from CSR GRACE/GRACE-FO RL06 Mascon solutions (version 02), China Gauge-based Daily Precipitation Analysis (CGDPA, version 1.0) data, and CN05.1 temperature dataset. The precipitation reconstruction model was established, and the seasonal and trend terms of CSR RL06 Mascon products were considered to obtain the dataset of terrestrial water storage anomalies in China. The data quality is good as a whole, and the uncertainty of most regions in China is within 5cm. This dataset complements the nearly one-year data gap between GRACE and GRACE-FO satellites, and provides a full time series for long-term land water storage change analysis in China. As the CSR RL06 Mascon product, the average value between 2004.0000 and 2009.999 is deducted from this dataset. Therefore, the 164-174 months (i.e., July 2017 to May 2018) of this dataset can be directly extracted as the estimation of terrestrial water storage anomalies during the gap period. The reconstruction method for the gap of JPL RL06 Mascon is consistent with that of CSR RL06 Mascon.

This dataset consists of rainfall scenarios and ensemble projections of extreme daily rainfall and mean summer season rainfall over Wanzhou County, China.

Precipitation Reference Period (1979-2018)

The reference scenario rainfall covers the period of 1979-2018, and is derived from the China Meteorological Forcing Dataset (https://data.tpdc.ac.cn/en/data/8028b944-daaa-4511-8769-965612652c49/). The extreme daily rainfall (in mm/day) is derived from Gumbel distributions fitted to monthly maximum daily rainfall covering the months of June to August. A spatial distribution of return periods from 2, 5, 10 20, 50 and 100 years for this scenario were derived and included in this dataset. The mean seasonal rainfall scenario covers the average daily rainfall (in mm/day) for the months of May to July to represent antecedent rainfall conditions of that could trigger shallow landslides during the summer season.

Spatial extent: Wanzhou County, China

Spatial Resolution: 0.1 degrees x 0.1 degrees

Time period: 1979-2018

Data Format: .csv files (.xyz file extensions)

Variable: Rainfall (pr)

Units: mm/day

Ensemble Projections and Climate Change Factors

The ensemble climate change projections cover two periods: Mid-21st Century (2021-2060) and Late-21st Century (2061-2100). The influence of climate change is assessed through climate change factors that represent a multiplicative factor of change between present and future climate model outputs. The ensemble projections are the mean climate change factor derived from four bias-corrected Regional Climate Model outputs. The ensemble consisted of the results REMO2015 and RegCM4 models that dynamically downscaled HadGEM2-ES, MPI-ESM-ML, and MPI-ESM-MR model outputs (https://esgf-data.dkrz.de/search/cordex-dkrz/). The bias correction was performed using the quantile delta method. An empirical transfer function for daily rainfall was used to derive the mean seasonal rainfall scenario, while a parametric (Gumbel distribution) transfer function was used to derive on the monthly maxima for the extreme daily rainfall scenarios.

Spatial extent: Wanzhou County, China

Spatial Resolution: 0.22 degrees x 0.22 degrees

Time periods: Mid-21st Century (2021-2060) & Late-21st Century (2061-2100)

Data Format: .csv files

Variable: Climate Change Factor (ccf)

Unit: Dimensionless

Included ensemble projection statistics:

Standard deviation (sd)

Coefficient of Variation (cv)

In 2023, the annual rainfall measured across India amounted to ***** millimeters. This was a decrease from around ***** millimeters of rainfall recorded one year earlier. The month of July saw the highest amount of rainfall in 2023 across the country.

This dataset is the 30m hydraulic erosion data of Chinese mainland from 1990 to 2022 (t/(hm2 a)), constructed based on Google Earth Engine and the Revised Universal Soil Loss Equation (RUSLE). The RUSLE formula includes five factors: Cover management factor(C), Soil erodibility factor(K), Slope length and steepness factor(LS), Rainfall erosivity factor(R), and Support practice factor(P). By multiplying these five factors, the soil erosion modulus can be obtained. Among them, a random forest interpolation method for rainfall data is introduced to obtain 30 m annual rainfall data and a multi-equation combined vegetation cover management factor tailored to the soil characteristics of mainland China is designed. Each zip file contains soil erosion data for 31 provinces in Chinese mainland. All files in this dataset are in TIFF format. Each province has been divided into multiple tiles based on its area size. To use this dataset, it is necessary to mosaic each tile of a province together. This dataset not only enables the public to have a more comprehensive and in-depth understanding of the serious harm of soil erosion but also provides regional, precise, and scientific basis for government decision-making.

This dataset is a raster dataset of precipitation in the Xiluodu reservoir area from 2001 to 2020 (the reservoir area is based on digital elevation model data and delineated along the ridge line). The original data of this dataset is the "1901-2024 China 1km Resolution Monthly Precipitation Dataset", sourced from the National Earth System Science Data Center (https://www.geodata.cn/). The original dataset is a NetCDF format file, and monthly raster data in TIFF format needs to be created in ArcGIS based on different bands. The annual dataset is obtained by accumulating and summing the raster data of all months within a specific year. This dataset adopts the GCS_WGS_1984 coordinate system, with a spatial resolution of 1km and units of 0.1mm.

The original data is the data interpolation product of China Meteorological Website: Surface precipitation monthly value grid data set in China.

614 pieces of literature for reptile occurrence records in China Results of this analysis