## 5 Conclusions  
Droughts are natural hazards that may aggravate in the coming years, especially because of the effects of climate change. Pakistan is highly vulnerable to the adverse impacts that result from droughts. Therefore, it is crucial to understand the behavior of droughts in Pakistan to plan accordingly. This research, therefore, improves the understanding of meteorological drought behavior in Pakistan.  
The Standardized Precipitation Evapotranspiration Index (SPEI) was chosen as an indicator of drought. Gridded precipitation and evapotranspiration data from Climate Research Unit (CRU), at a resolution of 0.5\({}^{\circ}\) by 0.5\({}^{\circ}\), was downloaded for the time period 1902-2015. SPEI was then calculated for each grid cell, followed by applying a clustering algorithm to group Pakistan into five homogeneous and contiguous zones based on SPEI. The run theory was employed to identify drought events where the threshold value of SPEI was taken to be \(-\)0.5. Spatial \"K\" luster Analysis using a Tree Edge Removal (SKATER) method was used to produce the meteorological drought homogeneous zones. The Modified Mann-Kendall trend test was applied to the SPEI and drought characteristics in each zone.  
Overall, the study sheds light on the variability of drought characteristics in different zones of Pakistan. Nevertheless, there were some limitations to this study. We only considered 3-month SPEI, which is suitable for short-term meteorological drought modeling. Other time scales need to be explored to provide a holistic view of the drought variability in Pakistan. In addition, the SPEI drought index used here was based on one specific dataset (CRU) of precipitation and other climate fields. A recent evaluation of numerous precipitation products in the Indus Basin, which includes most of Pakistan, found that the CRU precipitation field appears to have realistic mean amount and long-term trends relative to station observations, suggesting that temporal homogeneity and bias are on the whole successfully controlled by CRU's interpolation and quality control procedures [27]. However, a sensitivity analysis, possibly including intercomparison to SPEI derived from other historic climate datasets, would help confirm the drought patterns and trends seen here. Furthermore, drought indices developed specifically for agricultural and hydrological droughts, such as PDSI and Streamflow Drought Index (SDI), must be incorporated in future research to understand the transition among the different types of drought. This would greatly help in the understanding of different types of droughts and their interlinkages.  
Conceptualization, S.J., G.H.D., K.A., and N.Y.K.; Methodology, S.J., G.H.D., K.A., and N.Y.K.; Software and Visualization, S.J.; Writing--original draft, S.J.; Writing--review and editing, S.J., G.H.D., K.A., and N.Y.K.  
**Funding:** N.Y.K. was supported by the United States Agency for International Development through the U.S.-Pakistan Center for Advanced Studies in Water (USPCAS-W) and by the National Oceanic and Atmospheric Administration Educational Partnership Program with Minority-Serving Institutions--Cooperative Science Center for Earth System Sciences and Remote Sensing Technologies under the Cooperative Agreement Grant No. NA16 SEC4810008. In addition, authors would like to acknowledge the financial support for this study from the USPCAS-W, Mehran University of Engineering and Technology, Jamshoro, Pakistan established with the grant of United States Agency for International Development (USAID). The contents are the sole responsibility of the authors and do not necessarily reflect the views of USPCAS-W, USAID or the United States or Government of Pakistan.  
**Conflicts of Interest:** The authors declare no conflict of interest.