This paper reviews the Flash Drought concept, the uncertainties associated with FD prediction, and the potential of Machine Learning (ML) and Deep learning (DL) for future applications. For this, 121 relevant articles covering different aspects of FD ‐ definitions, key indicators, distinguishing characteristics, and the current methods for FD assessment (i.e., ‐ monitoring, prediction, and impact assessment) are examined. FD is typically a short‐term drought event ‐ characterized by the rapid progression of heat waves and precipitation deficits, causing cascading impacts on the land and surface hydrology. FD prediction is constrained by the lack of consistent FD definitions, key indicators, the limited predictability of FD at the subseasonal‐ to‐seasonal (S2S) timescale, and uncertainties associated with the current prediction methods. Some of the uncertainties in the current methods are associated with a lack of our understanding of the physical processes. They are also related to the error in the input datasets (imperfect representation of indicators), parameter uncertainty (parameterization scheme adopted by the prediction model), multicollinearity, nonlinear, and non‐stationary interactions among different indicators. Combining traditional methods and multisource fusion data with ML and DL methods shows promise to better understand FD evolution and improves prediction.

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<p>Water, food, and energy security are the major climate risks of global warming. The Paris Agreement proposed an ambitious target of limiting the rise in global mean surface temperature to well below 2<sup>0</sup>C, and preferably to 1.5<sup>0</sup>C, compared to the pre-industrial era. However, the implication of this policy discourse on the agricultural system is imperative for ensuring food security in the face of global warming. This research focuses on understanding the changes in water availability and rice productivity under 1.5<sup>0</sup>C global warming over a global rice-exporting semi-arid watershed in Central India. Towards this goal, the mean climate under 1.5<sup>0</sup>C of global warming was computed for 21 Coupled Model Intercomparison Project Phase 6 (CMIP6) Global Climate models (GCMs). For each GCM, the corresponding changes in blue-green water availability and rice productivity at 1.5<sup>0</sup>C warming period were estimated under two global warming scenarios (SSP2-4.5 and SSP5-8.5) based on the semi-distributed Soil and Water Assessment Tool (SWAT). Results suggest that the green and blue water is projected to change by ~ -20% to 10 and ~ -50 to 20%, respectively. The rice yield is projected to reduce in the range of 5% to 50%, with an increase in local temperature (~1<sup>0</sup>C) and a decrease in local precipitation (~20%) being the limiting factor. This study provides useful information on when the 1.5<sup>0</sup>C global warming could reach and how it can affect the agricultural productivity of semi-arid watersheds across different global warming scenarios. This study will help develop appropriate strategies to reduce/alleviate the impacts of global warming and foster food security at the watershed-scale.   </p>