<p>Although several methods are used to study and assess the threat of complex catastrophic events, many of them lead to a risk underestimation, sometimes with dramatic consequences. Another way to study extreme, rare events is by using the ranking statistics. Such a method focuses on the undersampled tail of a distribution and has been used to analyze earthquakes, hurricanes, and other natural catastrophic events. So far, it has not been used to study droughts. As extreme drought poses a threat to human health, economy, and survival, aside from its ecological effects, in this work a novel method for using ranking statistics in the assessment of the meteorological drought threat is presented and compared with the traditional drought assessment method of the Standardized Precipitation Index (SPI). The method is thereafter applied to the drought-prone region of Aguascalientes in Mexico, which can serve to identify local climate regions. The rank profile obtained here indicates that meteorological drought can be systematically classified into categories that are clearly separated by gaps on a log-log scale, suggesting the presence of underlying drivers, while the SPI underestimates extreme drought events. Thereafter, we use nonlinear regression to compare data with several ranking laws. It is found that the Weibull function provides a better fit to the data than other two-parameter fitting functions. An empirical analysis of the fitting parameters of such a function for nearby communities shows a linear relation between both parameters, revealing a negative correlation between the meteorological drought threat and the threat of rainy years. This provides a quantitative measure of the historical behavior of the meteorological drought threat that can serve as a spatial climatic index, giving a different perspective when compared with other traditional indices.</p>