## 1 Introduction  
With continuous developments in numerical weather forecasting technologies, numerical weather models have become a major tool for operational weather forecasting [1]. Model forecasting accuracy is also directly related to weather forecasting accuracy and further affects the effectiveness of meteorological services [2, 3]. At present, much research work is being carried out on numerical models such as the Weather Research and Forecast (WRF) model, including the optimization of physical parameterization schemes, the application of multi-source observation data, and improvements in assimilation algorithms [4]. However, because the initial and model boundary conditions, the physical process, the static surface data, and the mathematical description of the model framework all contain uncertainties, which bring errors into the numerical model, the output forecast is also subject to errors. To further improve forecast accuracy, post-processing techniques have been widely used to reduce errors in operational forecasting applications [5]. These techniques include N-day running average correction [6], N-day Kalman filter post-processing techniques [7], and analog-based bias-correction methods [8]. These relatively straightforward methods often work with a small amount of data for bias correction. For example, bias-corrected models can gauge air temperature, relative humidity, and other surface meteorological variables from a WRF model with only eight previously selected forecasts [5]. The model output statistics (MOS) technique has also often been used to fuse previous model forecast errors with large amounts of data. For example, [PERSON] et al. [9] improved air temperature forecast accuracy in a WRF model with an updated MOS method. Recently, the development of machine learning methods has made it possible to digest even larger amounts of data with relatively high accuracy [10]. The most popular machine learning models include random forest [11], gradient boosting [12], and deep neural networks [13]. For example, [PERSON] et al. [14] used a multivariate machine learning model to post-process temperature forecasts. Machine learning models have also exhibited competitive capabilities in a wide range of prediction research works [15; 16]. Studies have proven that the ensemble use of these models outperforms the use of each model alone [17]. Therefore, there is a need for ensemble models to improve model performance. However, this usually requires many trial-and-tests and various forms of expertise to tune up hyperparameters in order to determine how to construct an ensemble model from multiple candidate methods. To solve this tuning problem, in recent years, automatic an machine learning (AutoML) framework has been developing rapidly in the discipline of data science. Even though this approach is popular in model optimization [18], computer visions [19], and other areas, its application in MOS to improve model forecasting performance has been scarce. In Xinjiang, a regional meteorological forecast model, which has run operationally at the Xinjiang Meteorological Bureau since 2018, was developed by the Institute of Desert Meteorology in the China Meteorological Administration in collaboration with the National Center for Atmospheric Research (NCAR), and it uses the Advanced Research WRF (ARW) research model and WRF data assimilation (WRFDA), version 3.8.1. Precipitation in Xinjiang has obvious terrain-dependent characteristics. The frequency of precipitation in the mountainous areas is highest where there are good forecast skills and fewer humans. Greater numbers of people from the oasis economic zone pay attention to the weather forecast in relation to temperature and relative humidity, both of which are important for travel and agricultural irrigation. Differences in topographic features between reality and the forecast model affect a forecast's precision. Forecast revision is still required.