## 1 Introduction  
Rainfall is an important driver of the water cycle and a key index of the change of weather system [1; 2; 3]. Additionally, interrelated elements in the water cycle such as runoff, evapotranspiration, and soil moisture also influence water resources, climate, agriculture, and ecosystems in a region [4; 5; 6]. In recent decades, with the maturity of information collection methodologies such as remote sensing, satellites and communication technology, forecasting through meteorological or climatic patterns has become an effective means for alleviating hazards caused by extreme hydrological events [7; 8; 9]. The development of climate models [10; 11] has effectively linked the atmospheric, surface, and subsurface processes, and prolonged the foreseeing period of storm and flood forecasting. Even so, this is not sufficient to understand and predict how the complex components of the water cycle interact with the complexities of the landscape. First of all, the current atmospheric models mainly apply a relatively simple one-dimensional approach in simulating both the surface and the groundwater processes, while less consideration is given to the lateral interaction among soil, surface and undersurface hydrological features [12; 13]. Second, in a standalone hydrological model [14], the output of the climate model is used as the input data to drive the hydrological model, but the indirect connection between the land surface and the atmosphere is neglected [15]. The lack of a feedback mechanism leads to the separation of the atmospheric precipitation and subsequent land surface hydrological processes. At the same time, the lateral water flow generated in the underground interaction and the redistribution of soil moisture cannot be fed back to the atmosphere. A fully coupled modeling system [16] of the climate model and the hydrologic model is a novel development designed to make up for the aforementioned lack. In the fully coupled system, the climate model is coupled with the hydrologic model through the land surface model (LSM) of the same physical mechanisms, and the input and output of both models provide feedback to each other. The progressive fully coupled modeling system yields regulated topographically-driven soil moisture and land surface fluxes, thus improving the spatiotemporal correlations between the surface and lower atmospheric elements [6]. [PERSON] et al. [17] tried to connect the hydrology model ParFlow [18] with the Advanced Regional Prediction System (ARPS) [19] in an idealized experimental simulation. The results showed that the fully coupled system could be used to describe the spatiotemporal correlation between lower atmospheric variables, surface and water table depth. To solve the biochemical shortcomings of the hydrological model, [PERSON] et al. [20] introduced Noah LSM to connect ParFlow, and the Weather Research and Forecasting (WRF), tests were still carried out in an idealized experiment scenario and proved that an accurate runoff mechanism and lateral flow could change the spatial distribution of land surface and atmospheric fluxes. [PERSON] et al. [8] used the Community Land Model (CLM) to link Parflow with atmospheric model, the Consortium for Small-Scale Modeling (COSMO), to a week prediction in Ruhr basin, Germany. They found the potential of the fully coupled system in surface flux prediction. [PERSON] et al. [21] proposed a parallel, multicore, multiphysics, multiscale, fully distributed hydraulic model system (WRF-Hydro). WRF-Hydro sets up a multiscale quasi-3D land surface hydrological simulation system to improve the one-dimensional vertical generalization of water transport using the LSM Noah or Noah MP in the WRF model [21]. It could operate independently or be used for the coupling with WRF.