This work presents a novel method of threat assessment applied to the drought-prone region of Aguascalientes in Mexico. Extreme drought poses a risk to human health, economy, and survival, aside from the ecological effects. A way to study and assess the threat of complex catastrophic events such as earthquakes, hurricanes, etc. is to use 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 twoparameter fitting functions. An empirical analysis of the fitting parameters of such a function for some other nearby communities shows a linear relation between both parameters. Such a relationship has limited spatial validity. This suggests that the ranking fit can be effectively characterized by a single independent parameter within a region. This provides a quantitative measure of the meteorological drought threat that can serve as a climatic index. The ranking analysis presented here aids in the hydrological management of water basins, including the assistance for agricultural producers, and serves to assess the risk associated with extreme drought.

The socioeconomic sector increasingly relies on accessible and cost-effective tools for predicting climatic conditions. This study employs a straightforward decision tree classifier model to identify similar monthly ENSO (El Niño Southern Oscillation) conditions from December 2000 to November 2023, using historically monthly ENSO Indices data from December 1950 to November 2000 as a reference. The latter is to construct monthly precipitation hindcasts for Mexico spanning from December 2000 to November 2023 through historically high-resolution monthly precipitation rasters. The model’s performance is evaluated at a global and local scale across seasonal periods (winter, spring, summer, and fall). Assessment using global Hansen–Kuiper Skill Score and Heidkee Skill Score metrics indicates skillful performance across all seasons (>0.3) nationwide. However, local metrics reveal a higher spatial percent of corrects (>0.40) in winter and spring, corresponding to dry seasons, while a lower percent of corrects (<0.40) are observed in more extensive areas during summer and fall, indicative of rainy seasons, due to increased variability in precipitation. The choice of averaging method influences the degree of underestimations and overestimations, impacting the model’s variability. Spearman correlations highlight regions with significant model performance, revealing potential misinterpretations of high hit rates during winter and spring. Notably, during the fall, the model demonstrates spatial skill across most of Mexico, while in the spring, it performs well in the southern and northeastern regions and, in the summer, in the northwestern areas. Integration of accurate forecasts of ENSO Indices to predict precipitation months ahead is crucial for the operational efficacy of this model, given its heavy reliance on anticipating ENSO behavior. Overall, the empirical method exhibits great promise and potential for application in other developing countries directly impacted by the El Niño phenomenon, owing to its low resource costs.

The socioeconomic sectors increasingly rely on prompt and affordable tools to predict climatic conditions. This study uses a decision tree classifier model to identify similar monthly ENSO 3.4 indices from March 2001 to February 2011 through monthly historical ENSO3.4 indices (December 1950 – February 2001). Historical monthly ENSO3.4 indices were used to extract available high-resolution monthly historical precipitation grids (1950-2001) for Mexico and constructed monthly ensembles (March 2001 – February 2011 hindcasts). Formerly, a precipitation categorization of monthly observed and simulated precipitation (2001-2011 hindcasts) was employed. Thereafter, global vector data and spatial map assessments were performed seasonally (winter, spring, summer, and fall). Global KSS and HSS metrics indicated the model approach has skill, especially in the spring and fall. Spatial metrics exposed large areas with hit rates > 0.60 in winter and spring, though in winter and spring (dry seasons), it was due to the precipitation categorization hindcast and observed were within the first classification. Lower hit rates, but still acceptable (> 0.40), were detected in summer and fall (during the rainy season and at the end, respectively) because more precipitation classifications were tested and prone to miss. Spatial Spearman correlations provided specific areas of significant model performances and found that high hit rates during the winter and spring are spurious. Meanwhile, it was also found that during the fall the model approach is reliable in most of Mexico, followed by the spring, and in constraint areas in summer and winter across the country. It also appears that dominant ENSO neutral events followed by balanced ENSO cooling and warming events do not prefer the model skill. Overall, the empirical method shows promise (low-cost human and computational resources if ENSO indices expose certainty in its Lead months to forecast) and has the potential to be used in other developing countries influenced by El Niño phenomena.

El sector socioeconómico depende cada vez más de herramientas eficientes y accesibles para predecir condiciones agroambientales. Este estudio utiliza un modelo de clasificación de árbol de decisiones para identificar ciclos agrícolas similares al fenómeno climático de El Niño Oscilación del Sur. El objetivo es relacionar históricamente los rendimientos agrícolas nacionales de maíz y frijol de temporal en México con ENOS, por medio de los censos agrícolas primavera-verano (PV) de 1980-2014 e identificar los ciclos primavera-verano análogos al El Niño Oscilación del Sur y simular posteriormente los rendimientos de 2015-2023 (promedio de años análogos) para ambos cultivos. Adicionalmente, los valores de las simulaciones se ajustaron con métodos como la desviación de rendimientos y la tendencia con la prueba de Mann-Kendall, del ciclo anterior. Los resultados del error de porcentaje medio absoluto (MAPE) muestran un buen ajuste para el maíz (MAPE= 4%) y el frijol (MAPE= 14%) con el uso de las desviaciones, la incorporación de la tendencia mejora ligeramente el ajuste en el frijol (MAPE= 11%), pero no en el maíz (MAPE= 22%). La eficacia de estos métodos como pronóstico de rendimientos con meses de anticipación depende en gran medida de la precisión de los pronósticos de la temperatura de El Niño Oscilación del Sur. Este método empírico demuestra potencial para aplicarse en otros cultivos de temporal y regiones influenciadas por El Niño Oscilación del Sur, por lo que ofrece una herramienta útil para la antelación de impactos socioeconómicos relacionados con este fenómeno.