In the warm season, El Niño/Southern Oscillation (ENSO) causes periods with more rain in Northern Mexico during its positive phase, while less rainfall is recorded in the southern regions during the negative phase. This research study evaluates the variability of summer (July–September) precipitation and moisture fluxes under different ENSO scenarios in the Mexican Altiplano and coast of the state of Nayarit. The catchment of Rio San Pedro-Mezquital (SPM-RB) connects both regions. Using the Oceanic Niño Index (ONI), the years that signal change from El Niño to La Niña (1998), neutral conditions (2005), and strong (moderate) La Niña (1999) were selected to get an insight of ENSO impact on summer precipitation. For anomalies in the Altiplano, two additional contrasting years were analyzed—2006 (mostly dry) and 2010 (wet)—to determine moisture sources. Summer rainfall conditions in 1998 and 1999 had an opposite behavior between coastal Nayarit (wet) and the Altiplano (dry), while in 2005, rainfall deficits were observed in both regions. The moisture fluxes showed large divergence areas over central Mexico and the Southeastern United States in years of intense drought (1998 and 1999) caused by two high-pressure cells at middle levels of the troposphere (500 hPa). The moisture transport mechanisms into the Altiplano were related to atmospheric circulation at the upper level (200 hPa). The variations of the moisture fluxes from 2006 to 2010 are less strong at middle levels. The Eastern Pacific moisture convergence along the western coast of Mexico favors above-average rainfall anomalies in the coastal region but below-average anomalies in the Altiplano.

Due to the lack of in situ observations in mountainous locations, the use of remote sensing data is an alternative to analyze rainfall distribution patterns during the passage of major hurricanes. In this work, gridded precipitation data from the CHIRPS database are evaluated by comparing with observations from weather stations during the passage of category 3–5 hurricanes for the period 1980–2024. The comparison between estimated and observed values is performed by regression analysis and the use of K and K₀ coefficients. An advantage of using K-ratio and K₀-ratio is the identification of overestimated or underestimated precipitation in the pixel records. The distribution of daily precipitation helped in a more concise way to better understand how well CHIRPS reproduced the observed rainfall patterns. Results show that correlations between observations and database estimates are in the range of 0.40–0.76, for eastern Pacific hurricanes, and 0.49–0.78 for Atlantic hurricanes, all of which are statistically significant; however, these results do not imply congruence between observations and estimates since CHIRPS fails to adequately reproduce the position of the highest precipitation core. In the initial stages of a tropical cyclone, near-zero correlations between observations and estimates indicate that CHIRPS is not able to reproduce the observed rainfall. It is recommended to use CHIRPS with caution when the focus is on analyzing rainfall patterns during the development of intense tropical cyclones.