RESUMENSe analiza el efecto de El Niño-Oscilación del Sur (ENSO, por sus siglas en inglés) sobre la precipitación en México. A diferencia de estudios anteriores, en éste se maneja una mayor cantidad de datos y se cubre más extensamente el territorio mexicano. En este trabajo se emplearon los datos de precipitación diaria de la base de datos CLICOM a partir de 1961, actualizada hasta 2015. El periodo de estudio se dividió en dos fases: 1961 a 1990 y 1991 a 2013, y se consideraron también separadamente las épocas fría y seca del año (noviembre-abril), y la cálida y húmeda (mayo-octubre). De esta manera, la cantidad de estaciones que superan los criterios de cantidad de información continua para un periodo determinado, aumenta considerablemente. Se utiliza el coeficiente de correlación de Pearson con una significancia de 5% para encontrar la relación entre la precipitación y el índice multivariado de ENSO (MEI, por sus siglas en inglés). Los resultados se presentan en mapas donde se observan regiones con precipitación por arriba o debajo del promedio. Claramente se identifica la región noroeste de México con una relación directa entre MEI y precipitación; mientras que se observa una relación inversa en la parte que se encuentra al sur del paralelo 22º N, durante los meses de verano. En el periodo invernal existe un aumento generalizado de la precipitación conforme aumenta el MEI. Se muestra la distribución de la lluvia para periodos normales tanto de invierno como de verano. ABSTRACTThe effect of El Niño-Southern Oscillation (ENSO) on precipitation in Mexico is analyzed. Unlike previous studies, the amount of data used is larger and the Mexican territory is more widely covered. In this paper, daily precipitation from the CLICOM database updated to 2015 was used. The studied period spans from 1961 to 2013 and was divided into two periods: 1961-1990 and 1991-2013. For the same periods two separated seasons were considered: the cold and dry (November-April), and the warm and wet (May-October). Thus, the number of stations that exceed the amount of continuous information criteria for a certain period increases considerably. The Pearson correlation coefficient with a significance of 5% was used in order to test for the existence of a relationship between precipitation and the Multivariate ENSO Index (MEI). The results are presented in maps where regions of precipitation above or below average are observed. During the summer/ warm months, the northwestern region of Mexico is clearly identified with a direct relationship between MEI and precipitation, whereas an inverse relationship in the part that lies south of latitude 22º N is seen. In the winter/cold months, there is a general increase in precipitation with increasing MEI. Distributions of normal rainfall for both winter and summer are also shown.

An evaluation of precipitation estimations on the ground for individual rainfall events was carried out by comparing the 2B31 Tropical Rainfall Measuring Mission (TRMM) product versus a high‐density, rain gauge network deployed at the ground over a small (about 1000 km2) study area in a continental region characterized by complex topography and high altitude. This comparison, using categorical analysis, showed a good agreement for several skill parameters most frequently used in works of this type. In this paper, it is concluded that Odds Ratio Skill Score (ORSS) is a more reliable measure of skill for categorical statistics than other scores because it better reflects the agreement between the two data sets. Furthermore, ORSS allows one to test the significance of the results so it is possible to discriminate whether the resulting skill is due to pure chance (ORSS was significant in 70% of the cases studied). Although variance and mean analyses generally showed differences between data sets for both the amount and the distribution of rainfall rate over the study area, least squares fits indicate a very high and quite linear correlation for both the mean rainfall rate (r2 = 0.90) and the maximum amount of precipitation at a given point (r2 = 0.74). It is concluded that 2B31 TRMM data can be used in weather applications for the area studied here and others with complex orographical characteristics and also as a tool in the diagnosis of individual rain events in other regions where there are no other data sources available.

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