## 1 Introduction  
Atmospheric water vapor is one of the important parameters of surface atmospheric energy exchange, hydrological cycle and climate change [1, 2, 3]. There are many acronyms for atmospheric water vapor, such as TCWV (Total Column Water Vapor, TCWV), PW (Precipitable Water vapor, PW), TPW (Total Precipitable Water vapor, TPW) and so on. The acronym of TCWV is used in this study. As the main greenhouse gas in the atmosphere, the changes in TCWV have a significant impact on temperature and precipitation, and the TCWV is closely related to surface evaporation, cloud formation and circulation transportation [4; 5; 6; 7]. In addition, the prediction of TCWV is also of great significance in monitoring small scale disastrous weather. Therefore, the study of the temporal and spatial variation characteristics of TCWV and its prediction are of great importance to global climate change, human life, and production. The amount of water vapor in the atmospheric column, usually comprehensive water vapor or precipitable steam, can be measured by various techniques. Experts and scholars have carried out a lot of research on TCWV by combining the sounding observation data of meteorological stations, reanalysis data and the relevant empirical formulas. Many studies have shown that the TCWV in China has obvious inter-annual and inter-decadal variation characteristics, and the spatial-temporal distribution of TCWV are influenced by various factors such as seasons, topography and temperature. [PERSON] et al. [8] found that water vapor decreases from southeast to northwest, and it has shown a gradual increasing trend in the Third Pole. [PERSON] et al. [9] reported that the seasonal variation of water vapor content in Northwest China appears to be lowest in winter, followed by spring, then by autumn, and it is the highest in summer. [PERSON] et al. [10] concluded that the first and second EOF modes of tropical Cold Point Tropopause (CPT) variations are related to typical El Nino-Southern (ENSO) activity and sea surface temperature (SST) changes in the central Pacific, respectively. [PERSON] et al. [11] applied the Mann-Kendall trend test to analyze the spatiotemporal variations of precipitation. The result revealed that the rainfall amount (RA) increased with the increase of light rain in Northwest China and heavy rain in Southeast China. [PERSON] et al. [12] studied the characteristics of the summer atmospheric water cycle in China by using the ERA-Interim and MERRA reanalysis from 1979 to 2012. [PERSON] et al. [13] evaluated the temporal and spatial distribution characteristics of precipitable water (PW) in China, and it showed an overall downward spatial trend from the southeast to the northwest direction, and it was also found that PW was most closely related to precipitation in the northeastern region and the upper northwestern region. [PERSON] et al. [14] evaluated that the capture capabilities of two satellite data sets (version 6 of AIRS-only and AIRS/AMSU) and seven existing reanalysis datasets (MERRA, MERRA2, NCEP1, NCEP2, CFSR, JRA55, ERA-Interim) for the annual mean climatology, annual cycle and inter-annual variability of the TCWV over the Tibetan Plateau. Predicting the TCWV in all its forms will be a difficult process when global warming has combined with the variability of nature itself. There has been a great deal of previous research on water vapor prediction. [PERSON] et al. [15] examined the Zhengzhou, China region. They used a wavelet and hybrid model of the complementary ensemble empirical mode decomposition, recurrent neural networks and ARIMA models on an annual precipitation sequence. They found that the relative error of the forecasting in 2013-2017 was 14.1%. [PERSON] et al. [16] analyzed the precipitation forecast using a SARIMA model in the Mt. Kenya region. The forecasting evaluation statistics show that ME = \(-\)0.0053687, MSE = 0.96794, RMSE = 0.98384 and MAE = 0.75197. [PERSON] et al. [17] had done a comparative study of SARIMA and ARIMA for runoff in United States.