This work presents a novel method for using ranking statistics in the assessment of the meteorological drought threat, applied to the drought-prone region of Aguascalientes in Mexico, which can serve to identify local climate regions. Extreme drought poses a risk to human health, economy, and survival, aside from its ecological effects. One way to study and assess the threat of complex catastrophic events such as earthquakes, hurricanes, and others is by using ranking statistics. Such a method has not been used to analyze droughts. Here, such a tool is introduced to study an important case example in central Mexico: Aguascalientes City, which recently suffered from extreme drought conditions in 2022 and 2023. Therefore, in this work, the ranking of annual precipitations in Aguascalientes City is obtained. This analysis indicates that meteorological droughts can be classified into different phases, which are clearly separated by gaps in the ranking profile on a log-log scale. Thereafter, several ranking laws that fit both tails corresponding to dramatic events were compared to the data using nonlinear regression. 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 an 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.