## 1 Introduction  
Pakistan is one of the most vulnerable countries to the adverse effects of climate change. Global warming has led to worrisome increases in temperature across the country. The frequency and intensity of extreme events such as droughts, floods, and heatwaves are increasing [1]. Among the extreme events, drought is a frequent occurrence, occuring in about 4 out of 10 years in Pakistan [2]. The nation faced its worst drought--since its creation in 1947--in 1998, which lasted up till 2002 [3, 4]. This particular drought was due to an El Nino event, which started to develop in the Pacific ocean in 1997 [5]. Several indices have been used to model droughts. [PERSON] et al. [6] used the self-calibrated Palmer Drought Stress Index (PDSI) [7] to characterize drought events and establish a relationship between hydrological and meteorological droughts in South Africa. [PERSON] and [PERSON] [8] employed the Standardized Precipitation Index (SPI) [9] in their study to evaluate the spatiotemporal characteristics and patterns of droughts in Serbia. [PERSON] et al. [10] also used SPI at different time scales, along with Principal Component Analysis (PCA), to analyze the spatiotemporal variability of drought in Turkey using 80 years data from 1931 to 2010. [PERSON] and [PERSON] [11], [PERSON] et al. [12], and [PERSON] et al. [13] have used the Standardized Precipitation Index (SPI) to study spatiotemporal drought variability in Baluchistan and Pakistan. However, SPI and PDSI have some major limitations which restrict their use, especially in the context of climate change. PDSI has a fixed time scale and, therefore, multi-timescale aspects of drought cannot be studied by employing PDSI [14]. Precipitation-based indices such as SPI depend on two assumptions: that the variability in precipitation exceeds that of other variables which affect land surface moisture state, and that the other variables, such as temperature, are stationary. However, with global warming expected to increase global temperatures markedly [15], the temperature can no longer be assumed to be stationary. Therefore, to incorporate the effects of a changing climate and allow analyses at different timescales, a novel index, Standardized Precipitation Evapotranspiration Index (SPEI), was introduced by [PERSON] et al. [16], which combines the multi-temporal nature of SPI and its simple calculation procedure with the ability of PDSI to take into account the effect of changes in evaporation demand. Numerous studies focusing on drought monitoring and its spatiotemporal analysis have utilized this index [17; 18; 19; 20; 21; 22; 23]. [PERSON] [17] found SPEI to be one of the most-suited indices to monitor droughts in Pakistan, after comparing 15 indices. Moreover, [PERSON] and [PERSON] [20] analyzed the evolution of droughts in Czech Republic using SPEI from 1961-2010 for the agricultural season (April-September). They calculated SPEI at different time scales and concluded that SPEI possessed the capability to distinguish the effect of the increase in drought severity due to increasing temperature. [PERSON] et al. [21] regionalized droughts in China based on SPEI for 1961-2015. The use of SPEI allowed the researchers to conclude that increasing temperatures, along with reduced precipitation, were a major cause of droughts in China. Various studies were carried out to compare SPEI with other indices in the past. In 2015, for example, [PERSON] et al. [22] carried out a spatiotemporal analysis of drought in Ningxia Hui Autonomous Region, China using SPI and SPEI indices. Their study concluded that SPEI is more suitable to explore the link between climate change and drought variation than SPI. More recently, [PERSON] and [PERSON] [23] used SPEI to analyze spatial and temporal drought patterns in Oklahoma, the United States, from 1901-2014. PCA with VARIMAX rotation was applied to identify four spatially contiguous and homogeneous regions based on 6-month SPEI.