 The China Meteorological Assimilation Driving Datasets for the SWAT model (CMADS) is a public dataset developed by Prof. Dr. Xianyong Meng. CMADS incorporates LAPS/STMAS technologies and was constructed using various scientific methods and technologies, including data loop nesting, projection of resampling models, and bilinear interpolation. The CMADS series can be used to drive various hydrological models, such as SWAT, the Variable Infiltration Capacity (VIC) model, and the Storm Water Management Model (SWMM). Additionally, it allows users to conveniently extract a wide range of meteorological elements for detailed climatic analyses. Data sources for the CMADS series include nearly 40,000 regional automatic stations under China’s 2,421 national automatic and business assessment centers (Meng et al., 2017a). This extensive data collection ensures the wide applicability and improved accuracy of the CMADS datasets across East Asia, particularly in China. The CMADS series has been refined and corrected to match the specific input and driving data formats of SWAT models, thereby reducing the complexity faced by model builders. An index table of various elements encompassing all of East Asia was established for SWAT models, enabling direct use of the datasets without requiring format conversion or calculations using weather generators. This enhancement has led to significant improvements in the modeling speed and output accuracy of SWAT models (Meng et al., 2017b). The integration of air temperature, air pressure, humidity, and wind velocity data in CMADS was primarily achieved through the LAPS/STMAS system. Precipitation data were integrated using CMORPH’s global precipitation products and data from the National Meteorological Information Center of China, which includes daily precipitation records from 2,400 national meteorological stations and CMORPH satellite’s inversion precipitation products. The inversion algorithm for incoming solar radiation at the ground surface employs the discrete longitudinal method by Stamnes et al. (1988) for calculating radiation transmission (Shi et al., 2011). The resolutions for CMADS V1.0, V1.1, V1.2, and V1.3 were 1/3°, 1/4°, 1/8°, and 1/16°, respectively (Meng et al., 2016). The China Meteorological Assimilation Datasets for the SWAT model (CMADS) was completed over the period from January 1, 1980, to December 31, 2018, and has been used in many watersheds across East Asia (Meng et al., 2017b; Cao et al., 2018; Liu et al., 2018; Shao et al., 2018; Vu et al., 2018; Zhao et al., 2018; Zhou et al., 2018; Gao et al., 2018; Tian et al., 2018; Xu et al., 2019; Yuan et al., 2019; Zhang et al., 2020). Future plans for CMADS include extending it as a real-time product. References: 1.Meng, X.Y.; Wang, H.; Chen, J. Profound Impacts of the China Meteorological Assimilation Dataset for SWAT model (CMADS). Water. 11, 832. (2019). 2.Meng,X.Y.,Wang, H.; Shi, C.; Wu, Y.; Ji, X.Establishment and Evaluation of the China Meteorological Assimilation Driving Datasets for the SWAT Model (CMADS). Water .10,1555. (2018). 3.Meng, X.; Zhang, X.; Yang, M.; Wang, H.; Chen, J.; Pan, Z.; Wu, Y. Application and Evaluation of the China Meteorological Assimilation Driving Datasets for the SWAT Model (CMADS) in Poorly Gauged Regions in Western China. Water. 11, 2171.(2019). 4.Meng,X.Y.,Wang, H., et al. Investigating spatiotemporal changes of the land-surface processes in Xinjiang using high-resolution CLM3.5 and CLDAS: Soil temperature. Scientific Reports. 7, 13286. doi:10.1038/s41598-017-10665-8. (2017a). 5.Meng,X.Y.,Wang, H. Significance of the China Meteorological Assimilation Driving Datasets for the SWAT Model (CMADS) of East Asia. Water. 9, (10),765. doi:10.3390/w9100765. (2017b). 6.Meng,X.Y.,Dan, L.Y. & Liu, Z.-H. Energy balance-based SWAT model to simulate the mountain snowmelt and runoff – taking the application in Juntanghu watershed (China) as an example. J. Mt. Sci. 12(2), 368-381 (2015). 7.Stamnes, K., Tsay, S.C., Wiscombe, W. & Jayaweera, K. Numerically stable algorithm for discrete-ordinate method radiative transfer in multiple scattering and emitting layered media. Appl. Opt. 27(12), 2502-2509 doi: (1988). 8.Shi, C. X., Xie, Z. H., Qian, H., Liang, M. L. & Yang, X. C. China land soil moisture EnKF data assimilation based on satellite remote sensing data. Sci. China Earth Sci. 54(9),1430-1440 (2011). 9.Meng, X., Wang, H., Chen, J. et al. High-resolution simulation and validation of soil moisture in the arid region of Northwest China. Scientific Reports. 9, 17227.(2019). 10.Vu, T.T.; Li, L.; Jun, K.S..Evaluation of MultiSatellite Precipitation Products for Streamflow Simulations: A Case Study for the Han River Basin in the Korean Peninsula, East Asia. Water.10, 642.(2018). 11.Liu, J.; Shanguan, D.; Liu, S.; Ding, Y.Evaluation and Hydrological Simulation of CMADS and CFSR Reanalysis Datasets in the QinghaiTibet Plateau. Water.10, 513.(2018). 12.Cao, Y.; Zhang, J.; Yang, M.Application of SWAT Model with CMADS Data to Estimate Hydrological Elements and Parameter Uncertainty Based on SUFI-2 Algorithm in the Lijiang River Basin, China. Water.10, 742.(2018). 13.Shao, G.; Guan, Y.; Zhang, D.; Yu, B.; Zhu, J.The Impacts of Climate Variability and Land Use Change on Streamflow in the Hailiutu River Basin. Water.10, 814.(2018). 14.Zhou, S.; Wang, Y.; Chang, J.; Guo, A.; Li, Z.Investigating the Dynamic Influence of Hydrological Model Parameters on Runoff Simulation Using Sequential Uncertainty Fitting-2-Based Multilevel-Factorial-Analysis Method. Water. 10, 1177. (2018). 15.Gao, X.; Zhu, Q.; Yang, Z.; Wang, H.Evaluation and Hydrological Application of CMADS against TRMM 3B42V7, PERSIANN-CDR, NCEP-CFSR, and Gauge-Based Datasets in Xiang River Basin of China. Water. 10, 1225. (2018) 16.Tian, Y.; Zhang, K.; Xu, Y.-P.; Gao, X.; Wang, J. Evaluation of Potential Evapo-transpiration Based on CMADS Reanalysis Dataset over China. Water. 10, 1126.(2018). 17.Qin, G.; Liu, J.; Wang, T.; Xu, S.; Su, G.An Integrated Methodology to Analyze the Total Nitrogen Accumulation in a Drinking Water Reservoir Based on the SWAT Model Driven by CMADS: A Case Study of the Biliuhe Reservoir in Northeast China. Water. 10, 1535. (2018). 18.Guo, B.; Zhang, J.; Xu, T.; Croke, B.; Jakeman, A.; Song, Y.; Yang, Q.; Lei, X.; Liao, W. Applicability Assessment and Uncertainty Analysis of Multi-Precipitation Datasets for the Simulation of Hydrologic Models. Water. 10, 1611(2018). 19.Dong, N.P., Yang, M.X., Meng,X.Y.,Liu, X.et al. CMADS-Driven Simulation and Analysis of Reservoir Impacts on the Streamflow with a Simple Statistical Approach. Water. 11(1), 178 (2018). 20. Guo, D.; Wang, H.; Zhang, X.; Liu, G. Evaluation and Analysis of Grid Precipitation Fusion Products in Jinsha River Basin Based on China Meteorological Assimilation Datasets for the SWAT Model. Water. 11, 253 (2019). 21.Yuan, Z.; Xu, J.; Meng, X.; Wang, Y.; Yan, B. Impact of Climate Variability on Blue and Green Water Flows in the Erhai Lake Basin of Southwest China.Water.11, 424. (2019). 22. Li, Y.; Wang, Y.; Zheng, J.; Yang, M. Investigating Spatial and Temporal Variation of Hydrological Processes in Western China Driven by CMADS. Water. 11, 435. (2019). 23. Zhao, X.; Xu, S.; Liu, T.; Qiu, P.; Qin, G. Moisture Distribution in Sloping Black Soil Farmland during the Freeze–Thaw Period in Northeastern China. Water. 11, 536.(2019). 24. Liu, X.; Yang, M.; Meng, X.; Wen, F.; Sun, G. Assessing the Impact of Reservoir Parameters on Runoff in the Yalong River Basin using the SWAT Model. Water. 11, 643. (2019). 25.Zhao, F.; Wu, Y.Parameter Uncertainty Analysis of the SWAT Model in a MountainLoess Transitional Watershed on the Chinese Loess Plateau. Water.10, 690.(2018).26.Zhang, L.; Meng, X*.; Wang, H*.; Yang, M.; Cai, S. Investigate the Applicability of CMADS and CFSR Reanalysis in Northeast China. Water. 12, 996. (2020). 27.Xu, X., Gao, P., Zhu, X., Guo, W., Ding, J. et al. Design of an integrated climatic assessment indicator (ICAI) for wheat production: a case study in Jiangsu province, china. Ecological indicators, 101, 943-953.(2019). 28.Yuan, X. F; Han,J.C, Shao, Y. et al.Geodetection analysis of the driving forces and mechanisms of erosion in the hilly-gully region of northern Shaanxi Province.Journal of Geographical Sciences. 29(5), 779-790.(2019). 29. Gao, X., Ming,G., Yang, Z. et al. Temperature dependence of extreme precipitation over mainland China. Journal of Hydrology. 583,124595.(2020). 30.Jiang, S. H., R. L. Liu, L. L. Ren, et al. Evaluation and hydrological application of CMADS reanalysis precipitation data against four satellite precipitation products in the upper Huaihe River basin, China. J. Meteor. Res., 34(5), 1096–1113. (2020). Call for Papers The following CMADS special issue journals are currently accepting or have closed for submissions. [Water] IF3.4- Special Issue "Utilization of China Meteorological Assimilation Driving Datasets for the SWAT Model Long Series (CMADS-L) in East Asia. Contact Editor: Prof. Xianyong Meng".(Deadline: 15 June 2025)
[Atmosphere] IF2.686- Special Issue " Modeling Applications under Changing Climatic Conditions on Water Resource Security ". Contact Editor: Prof. Xianyong Meng.(Deadline: 30 December 2024)
This website allows users to download CMADS data in SWAT file format for specific locations and time periods. In CMADS V1.0 (with a spatial resolution of 1/3°), East Asia was divided into 195 × 300 grid points, encompassing 58,500 stations. Although CMADS V1.1 maintains the same temporal resolution as V1.0, it includes more data with 260 × 400 grid points, totaling 104,000 stations. CMADS V1.2 further increases the spatial resolution to 520 × 800 grid points, comprising 416,000 stations. For SWAT model users, we provide data including daily average relative humidity (fraction), daily accumulated 24-hour precipitation (mm), daily average solar radiation (MJ/m²), daily maximum and minimum temperature (°C), and daily average wind speed (m/s). Additionally, for users of other models, we offer data such as daily average atmospheric pressure (hPa), daily average temperature (°C), daily average specific humidity (g/kg), and more. We kindly suggest citing the following CMADS references to ensure academic integrity. 1.Meng, X.Y.; Wang, H.; Chen, J. Profound Impacts of the China Meteorological Assimilation Dataset for SWAT model (CMADS). Water.11, 832. (2019). 2.Meng,X.Y.,Wang, H. Significance of the China Meteorological Assimilation Driving Datasets for the SWAT Model (CMADS) of East Asia. Water. 9, (10),765. (2017). 3.Meng, X.; Wang, H.; Shi, C.; Wu, Y.; Ji, X.Establishment and Evaluation of the China Meteorological Assimilation Driving Datasets for the SWAT Model (CMADS). Water. 10,1555. (2018). 4.Meng, X.; Zhang, X.; Yang, M.; Wang, H.; Chen, J.; Pan, Z.; Wu, Y. Application and Evaluation of the China Meteorological Assimilation Driving Datasets for the SWAT Model (CMADS) in Poorly Gauged Regions in Western China. Water. 11, 2171.(2019). 5.Meng,X.Y.,Wang, H., et al. Investigating spatiotemporal changes of the land-surface processes in Xinjiang using high-resolution CLM3.5 and CLDAS: Soil temperature. Scientific Reports. 7, 13286.(2017). 6.Meng, X., Wang, H., Chen, J. et al. High-resolution simulation and validation of soil moisture in the arid region of Northwest China. Scientific Reports. 9, 17227.(2019). 7.Zhang, L.; Meng, X*.; Wang, H*.; Yang, M.; Cai, S. Investigate the Applicability of CMADS and CFSR Reanalysis in Northeast China. Water. 12, 996. (2020).  CMADS V1.0 Total data: 49GB Occupied space:49GB Time: From year 2008 to year 2018 Time resolution: Daily Geographical scope description: East Asia Longitude: 60°E The most east longitude: 160°E North latitude: 65°N Most southern latitude: 0°N Number of stations: 58500 stations Spatial resolution: 1/3 * 1/3 * grid points  CMADS V1.1 Total data: 77GB Occupied space: 77GB Time: From year 2008 to year 2018 Time resolution: Daily Geographical scope description: East Asia Longitude: 60°E The most east longitude: 160°E North latitude: 65°N Most southern latitude: 0°N Number of stations: 104,000 stations Spatial resolution: 1/4 * 1/4 * grid points Download CMADS V1.1 (BD-Cloud) Download CMADS V1.1 (Chinese)  CMADS V1.2 Total data: 200GB Occupied space: 200GB Time: From year 2008 to year 2018 Time resolution: Daily Geographical scope description: East Asia Longitude: 60°E The most east longitude: 160°E North latitude: 65°N Most southern latitude: 0°N Number of stations: 416,000 stations Spatial resolution: 1/8 * 1/8 * grid points Download CMADS V1.2 (BD-Cloud) Access code: CMAD  CMADS-L V1.0 Total data: 159GB Occupied space: 159GB Time: From year 1979 to year 2018 Time resolution: Daily Geographical scope description: East Asia Longitude: 60°E The most east longitude: 160°E North latitude: 65°N Most southern latitude: 0°N Number of stations: 58,500 stations Spatial resolution: 1/3 * 1/3 * grid points Download CMADS-L V1.0 (BD-Cloud) Access code: CMAD  CMADS-L V1.1 Total data: 350GB Occupied space: 350GB Time: From year 1979 to year 2018 Time resolution: Daily Geographical scope description: East Asia Longitude: 60°E The most east longitude: 160°E North latitude: 65°N Most southern latitude: 0°N Number of stations: 104,000 stations Spatial resolution: 1/4 * 1/4 * grid points  CMADS-ST V1.0 Total data: 12GB Occupied space: 12GB Time: From year 2009 to year 2013 Time resolution: Daily Geographical scope description: East Asia Longitude: 60°E The most east longitude: 160°E North latitude: 65°N Most southern latitude: 0°N Number of stations: 58,500 stations Spatial resolution: 1/3 * 1/3 * grid points Download CMADS-ST V1.0 (BD-Cloud)
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