## 1 Introduction
The studies on drought can be varied, ranging from monitoring, mitigation studies like vulnerability, risk, time series modeling and forecasting [12]. However, the key aspect of drought which makes it different from other hazards is it takes time to reflect its effects on any economic or agricultural sector. Therefore, forecasting becomes an interesting prospect as a reliable modelcould help in mitigating some effects of the drought. For this purpose, various techniques have been applied, like physical [21], stochastic [22], probabilistic [23] and data-driven [11] ones. However, among these data-driven models involving machine learning (ML) algorithms are comparatively less computationally intensive and could provide sufficient understanding without the requirement of extensive dataset types. Although the forecasting capabilities of physical-based models are accurate for atmospheric factors like temperature, they are less accurate for essential drought affecting parameters like rainfall [24]. Moreover, physical models are difficult to implement, as they require various data types involving complex models and require intensive computation power [25]. ML-based models have been used for drought forecasting for different drought affected regions of the world. As an example, [PERSON] and [PERSON] [26] used decision trees, Random Forest and extremely randomized trees for meteorological drought forecasting for South Korea. Similarly, [PERSON] et al. [27] used Random Forest to forecast severe drought for Western Korea. Of the various ML models, Random Forests (RFs) have the capability to handle large datasets involving multiple features, as is the case for the present study. RF has shown significant advantage over other supervised learning methods as it has the ability to handle highly non-linearly correlated data, robustness to noise and opportunity for efficient parallel processing [28]. Furthermore, the RF model has other important features, like intrinsic feature selection step and prior application to classification problem, and it can reduce variable space by providing an importance value for every feature. The use of RF models has been extensively carried out for various natural hazard studies for both regression and classification purposes, such as landslide [25], floods [29], earthquakes [30] and soil erosion [31]. The occurrence of a drought initiates with the deficiency in rainfall and the change in global climatic variables has had a serious effect on drought severity and longevity. Therefore, researchers have started to focus on the use of climatic variables, which could provide a better idea about drought trends both in spatial and temporal context. [PERSON] and [PERSON] [10] used climatic indices, along with sea surface temperatures (SSTs), as they trigger rainfall availability, to understand drought occurrences for the Eastern Australia region. Similarly, [PERSON] and [PERSON] [8] used hydro-meteorological, climate, sea surface temperatures and topographic variables to forecast drought for the Upper Blue Nile region of Ethiopia. The current work uses precipitation, potential evapotranspiration, vapor pressure, cloud cover and temperature (maximum, minimum and mean) variables for forecasting purposes. However, most of the studies in the literature [2,8,32] have not used cloud cover and vapor pressure as a variable for drought forecasting, even though it is very relevant for hydrological modeling and vegetation health. It is important to note that most of the studies are conducted based on ground-based data, and the availability of cloud cover variable can also be of concern. Moreover, the study has shown the importance of climatic indices and sea surface temperatures; however, the present study did not use such indices, and future works would involve these variables, which would help to provide a better understanding of the drought forecasting model and the important variables.