The increasing occurrence of severe droughts and sparse rainfall due to climate change is resulting in high and costly demand for water resources. A greater understanding of uncertainties associated with the creeping occurrence of agricultural drought and proactive planning to mitigate drought impact hence needs a probabilistic assessment. This study presents the probabilistic characterization of agricultural drought, depicted by the cumulative soil moisture deficit above a threshold (Severity) and water stress in the plants (WSP‐sum) during dry periods (duration). The concept of Crop Growing Process (CGP) for specified crops is introduced and defined as a continuous flow of water from the soil through the roots of the plant to the leaves then transformed into water vapor and finally released. The CGP is a two‐state process, either a normal or subnormal state. The probability density function (PDF) of the WSP‐sum manifests the overall subnormal state of CGP over the duration by means of the failure probability estimated as the sum of two distinct parts from two independent phases. Phase1 estimates the part of the failure probability influenced by the average duration; and Phase‐2 estimates the remaining part influenced by the average severity. The phase with the smallest rate parameter determines the WSP‐sum, which indicates whether a premature end of CGP was due to drought or the CGP survived until the end of the cropping season. The predicted designed drought components for specified return periods are presented to assist in proactive drought management. The combined influence of all the drought components upon the WSP‐sum probabilistically characterizes agricultural drought, given the CGP.