Improving Land Surface Hydrological Simulations in China Using CLDAS Meteorological Forcing Data Jianguo LIU 1,4 , Chunxiang SHI 2* , Shuai SUN 2 , Jingjing LIANG 3 , and Zong-Liang YANG 4 1 School of Mathematics and Computational Science, and Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture in Hunan Province, Huaihua University, Huaihua 418008, China 2 National Meteorological Information Center, China Meteorological Administration, Beijing 100081, China 3 Key Laboratory of Regional Climate–Environment Research for Temperate East Asia, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China 4 Department of Geological Sciences, The John A. and Katherine G. Jackson School of Geosciences, The University of Texas at Austin, Austin, TX 78712, USA (Received May 8, 2019; in final form August 23, 2019) ABSTRACT The accuracy of land surface hydrological simulations using an offline land surface model (LSM) depends largely on the quality of the atmospheric forcing data. In this study, Global Land Data Assimilation System (GLDAS) for- cing data and the newly developed China Meteorological Administration Land Data Assimilation System (CLDAS) forcing data are used to drive the Noah LSM with multiple parameterizations (Noah-MP) and to explore how the newly developed CLDAS forcing data improve land surface hydrological simulations over mainland China. The monthly soil moisture (SM) and evapotranspiration (ET) simulations are then compared and evaluated against obser- vations. The results show that the Noah-MP driven by the CLDAS forcing data (referred to as CLDAS_Noah-MP) significantly improves the simulations in most cases over mainland China and its eight river basins. CLDAS_Noah- MP increases the correlation coefficient (R) values from 0.451 to 0.534 for the SM simulations at a depth range of 0–10 cm in mainland China, especially in the eastern monsoon area such as the Huang–Huai–Hai Plain, the southern Yangtze River basin, and the Zhujiang River basin. Moreover, the root-mean-square error is reduced from 0.078 to 0.068 m 3 m −3 for the SM simulations, and from 12.9 to 11.4 mm month −1 for the ET simulations over mainland China, especially in the southern Yangtze River basin and Zhujiang River basin. This study demonstrates that, by merging more in situ and remote sensing observations in regional atmospheric forcing data, offline LSM simulations can better simulate regional-scale land surface hydrological processes. Key words: hydrological simulations, Noah-MP, atmospheric forcing, China Meteorological Administration Land Data Assimilation System (CLDAS), Global Land Data Assimilation System (GLDAS) Citation: Liu, J. G., C. X. Shi, S. Sun, et al., 2019: Improving land surface hydrological simulations in China using CLDAS meteorological forcing data. J. Meteor. Res., 33(6), 1194–1206, doi: 10.1007/s13351-019-9067-0. 1. Introduction While covering about 30% of the earth’s surface, land plays a vital role in modulating the global water cycle, energy cycle, and the carbon cycle (Koster et al., 2000, 2004; Oleson et al., 2008; Wang et al., 2016). Obtaining accurate high-resolution spatiotemporal land surface hy- drological information improves weather forecasts and seasonal climate predictions, and allows us to better monitor extreme events such as drought and floods (Rob- ock et al., 1998; Koster et al., 2000, 2004; Albergel et al., 2012; Wang et al., 2016). Currently, three major methods are used to obtain land surface states and fluxes: in situ observations, satellite re- trievals, and land surface model (LSM) simulations, where each has its own advantages and disadvantages. In Supported by the National Natural Science Foundation of China (91437220 and 41405083), Project Fund from the Education Depart- ment of Hunan Province (14C0897), and Huaihua University Double First-Class Initiative in Applied Characteristic Discipline of Con- trol Science and Engineering. *Corresponding author: shicx@cma.gov.cn. ©The Chinese Meteorological Society and Springer-Verlag Berlin Heidelberg 2019 Volume 33 Special Collection on Development and Applications of Regional and Global Land Data Assimilation Systems DECEMBER 2019