This work presents a new approach to defining drought, establishing an empirical relationship between historical droughts (and wet spells) documented in impact reports, and a broad range of observed drought-related climate features. A Random Forest (RF) algorithm was trained to identify the particular combinations of predictors – such as precipitation, soil moisture and potential evapotranspiration – that led to categorical, documented drought or non-drought events. Unlike traditional drought definitions, the new RF drought indicator combines meteorological, hydrological, agricultural, and socioeconomic drought, providing drought information for all impacted sectors. The metric also quantifies the conditional probability of drought (rather than being threshold-based), considering multiple climate features and their interactive effect, and can be used for forecasting. The approach was validated out-of-sample across several random selections of training and testing datasets, and demonstrated better predictive capabilities than commonly used drought indicators in a range of performance metrics. Furthermore, it showed a comparable performance to the (expert elicitation-based) US Drought Monitor (USDM) which is the current state-of-the-art record of historical drought in the USA. As well as providing an alternative historical drought indicator to USDM, the RF approach offers additional advantages by being automated, by providing drought information at the grid-scale, and by having predictive capacity. As a proof-of-concept case, the RF drought indicator was trained on Texan climate data and droughts, and validated in all Texas ecoregions. However, the introduced approach can be easily implemented to develop a RF drought indicator for new regions if adequate information on historical droughts is available.