## 1 Introduction  
Droughts can be categorized as one of the most devastating natural hazards, as they can affect regional, or even national, scales [1; 2; 3]. The challenges toward drought studies are enormous, primarily due to the unavailability of a fixed definition. Different researchers in their respective fields have defined droughts based on their own requirements [4; 5]. As an example, the deficiency in rainfall could alarm a meteorologist; however, for a hydrologist, drought occurrence begins when there is a reduction in stream flow and similarly is the case for agricultural and socioeconomic fields [4; 6]. Moreover, the effect of climate change and anthropogenic activities has led to the occurrence of more severe and extended drought periods [7]. The analysis of various drought aspects has seen tremendous progress with the use of different climatic [8], remote sensing dataset [9] and the inclusion of various variables [10], which could potentially lead to a better understanding of a particular drought type. However, accurate forecasting of droughts is still a challenge, and researchers are looking into different aspects which could reflect the ground reality [11]. Since defining a drought is important, indices were developed based on certain variables and their dependences for the drought type being analyzed [11; 12]. These can provide a path toward understanding drought. Over the years, several drought indices have been developed, using different variables. [PERSON] et al. [13] have provided a list of indices with their pros and cons. Of the several indices, one that is popular among researchers is the Standard Precipitation Evaporation Index (SPEI), developed by [PERSON] et al. [14]. The use of SPEI over other used indices is due to its involvement of both rainfall and temperature as factors, unlike Standard Precipitation Index (SPI), which only uses precipitation data. The use of SPEI has been tested in various parts of the globe and for different drought studies [15]. The performance of SPEI over other prominent drought indices like SPI and Palmer Drought Severity Index (PDSI) has been widely tested for several parts of the world under different climatic regions, with contrasting results. For instance, [PERSON] [16] recommended using SPI for humid regions and SPEI for arid regions for a study based in India, whereas [PERSON] [17] suggested to use SPEI over SPI and PDSI to identify drought conditions based on the study conducted at a global scale. Moreover, the use of SPEI in identifying the drought regions during the recent mega-fires in Australia has been tested by [PERSON] et al. [18] and was found to be an effective indicator. The calculation of the index could involve either the use of ground-based data, which can suffer from inhomogeneity due to its sparse location, or satellite-based, data which covers a large area but with variable bias and gap of data due to cloud or satellite return frequency. Although ground-based data has its own benefits especially when understanding at local scale, satellite-based data can also provide similar or even better understanding of the variables when studying at the regional level, which is the case for droughts. The use of global climatological datasets for drought studies is on the rise, and among the various available datasets, the Climate Research Unit (CRU) dataset is quite popular, owing to its longer time scale and finer spatial resolution. [PERSON] et al. [19] tested 30 different global climatological datasets, and their variation in precipitation values found that reanalysis climatic datasets have the most discrepancy compared to other models. Therefore, the present study uses the CRU dataset, and the various associated and derived climatological variables from 1901 to 2018, at a spatial resolution of 0.5\({}^{\circ}\)[20], with a temporal resolution of one month for drought index calculation and forecasting purposes.