## 1 Introduction  
Droughs, which last for several days or even years, are complex and recurring natural phenomena [1]. Their formation is mainly due to a substantial decline in water availability caused by insufficient precipitation [2, 3]. Global warming and climate change exacerbate droughs [4, 5]. Severe and persistent droughs can lead to ecological disasters [6] and pose serious risks to normal human life. A report shows that approximately 800,000 people died from droughs worldwide between 1970 and 2017 [7]. Effective spatiotemporal assessment of droughs can provide ecological knowledge and help in reducing the impact of drought. It is conducive to regional water resource management and decision-making with regard to ecological water use planning and ultimately alleviates the drought situation and reduces economic losses [8]. Meteorological station recordings and remote sensing data are two widely used data sources for drought-related research over the past decades [9, 10, 11, 12, 13]. Remote sensing data present many advantages when used for large areas that lack meteorological stations. However, several uncertainties due to algorithms based on derived products from satellite imageries [14, 15] and cloud contamination [16] limit the accuracy of such data. For example, precipitation products from remote sensing mainly rely on passive microwave or infrared technologies [17; 18], which are likely to be affected by topography and thus induce uncertainties in data quality. In addition, remote sensing precipitation products usually have a coarser spatial resolution; for example, the Tropical Rainfall Measuring Mission (TRMM) and the Climate Precipitation Center's morphing technique precipitation product have a spatial resolution of 0.25\({}^{\circ}\)[19], which is unsuitable for revealing the details of spatial heterogeneity in a given region. Therefore, using meteorological station recordings to monitor drought status has become a popular method, especially for mountain areas with heavy clouds or rainy weather. Furthermore, meteorological stations usually have long historical records and can establish a long-term drought parameter sequence, which is useful for exploring drought characteristics. Hydrological and statistical models have been established for drought monitoring and climate response analysis. Wavelet analysis [8; 20], the soil and water assessment tool (SWAT), the run theory [21], and cluster analysis [2] are commonly used models for monitoring drought. Amongst them, SWAT is currently the most widely used hydrological model. It uses elevation data to divide a watershed into sub-units, which are composed of all areas with similar landscape characteristics [22]. Combining SWAT with the drought index is found useful in investigating the impact of climate on future and past droughs in multiple regions [23]. However, the data input format of soil and water assessment tools to be used in these models have the restriction of a specific format, besides the requirement of data in days and the need for real data for the sake of verification, which makes this approach inconvenient to use. Wavelet analysis is a statistical model that explores the changes in drought over time by acquiring spectrograms. For time series with unequal intervals, least-squares wavelet analysis [24] and least-squares cross wavelet analysis [8] are applied in drought monitoring based on the drought indices. Using drought indices derived from the meteorological station observations to monitor drought situations has a long history and is proven to be effective in exploring drought spatiotemporal distribution patterns [2; 3; 25]. In the past decades, scientists have developed many drought indices by utilizing meteorological station recordings; examples include the precipitation condition index (PCI) [26], temperature condition index (TCI) [27], standardized precipitation index (SPI) [28], and standardized precipitation evapotranspiration index (SPEI) [29].