In the state of Sinaloa, rainfall presents considerable irregularities, and the climate is mainly semiarid, which highlights the importance of studying the sensitivity of various indices of meteorological drought. The goal is to evaluate the sensitivity of four indices of meteorological drought from five weather stations in Sinaloa for the prediction of rainfed maize yield. Using DrinC software and data from the period 1982–2013, the following were calculated: the standardized precipitation index (SPI), agricultural standardized precipitation index (aSPI), reconnaissance drought index (RDI) and effective reconnaissance drought index (eRDI). The observed rainfed maize yield (RMYob) was obtained online, through free access from the database of the Agrifood and Fisheries Information Service of the government of Mexico. Sensitivities between the drought indices and RMYob were estimated using Pearson and Spearman correlations. Predictive models of rainfed maize yield (RMYpr) were calculated using multiple linear and nonlinear regressions. In the models, aSPI and eRDI with reference periods and time steps of one month (January), two months (December–January) and three months (November–January), were the most sensitive. The correlation coefficients between RMYob and RMYpr ranged from 0.423 to 0.706, all being significantly different from zero. This study provides new models for the early calculation of RMYpr. Through appropriate planning of the planting–harvesting cycle of dryland maize, substantial socioeconomic damage can be avoided in one of the most important agricultural regions of Mexico.

The largest number of tropical cyclones (TCs) is generated in the northeastern Pacific Basin. These storms can produce extreme precipitation (EP) in northwestern Mexico, causing loss of life and environmental damage. It is important to understand the dynamics that cause the EP associated with TCs, since most human activity requires planning to adjust to the dynamics of local climate changes. Therefore, in this work the goal was to estimate the trends and return periods of the average annual daily extreme precipitation (AADEP; 95th percentile, P95) in the June-September season in the core North American monsoon. To do this, daily precipitation data from 1961 to 2000 from 48 climate computing (CLICOM) weather stations located in the core of the North American monsoon were used to determine AADEP:1. Non-parametric trends with Mann-Kendall tests and Sen's slope estimator. 2. Linear trends of annual averages of 95 (P95) and 99 (P99) percentiles with the least squares method. 3. Return periods with the Gumbel frequency distribution function. The results disclose a significant upward trend in the intensity of P95 increases in mountain stations, which may be related to a greater contribution of precipitation associated with TCs. The seasonal contribution of P95 in coastal stations and the total monsoon precipitation did not show statistical significance at α = 0.05. The return periods of P95 associated and not associated with TC's from 2005 to 2500 were calculated. Return periods of P99 have been rising since 2010 and will continue to 2500. For P95 events associated with TCs, the anomalies are expressed with synoptic conditions of simultaneous positive anomalies in the Pacific Decadal Oscillation (+PDO), negative anomalies in the

Abstract:The goal of this study was to generate a method to examine seasonal variability by climatic classification and Pacific seasonal factors to identify extreme wet and dry events in northern Mexico for the period 1952-2013. Using the standardized precipitation and evapotranspiration index (SPEI) on scales of three months (SPEI-3) and 24 months (SPEI-24), the variability of extreme wet and dry events were measured. The SPEI-3 and SPEI-24 anomalies were divided by the standard deviation (standardized Z anomalies). A Pearson correlation for SPEI-3, SPEI-24, Pacific decadal oscillation (PDO) and the oceanic El Niño index (ONI) was applied. Wet extreme events were recorded in

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