The goal was to model the trend of meteorological droughts (MeD) using the Pacific decadal oscillation (PDO) and Atlantic multidecadal oscillation (AMO) indices. PDO–AMO series were obtained from the National Oceanic and Atmospheric Administration. In 12 weather stations in the state of Sinaloa (1981–2017), the agricultural standardized precipitation index (aSPI) and reconnaissance drought index (RDI) were calculated. The linear (SLT) and non-parametric (SNT) significant trends of the aSPI and RDI were calculated. A principal component analysis was applied to SLT–SNT and the first observed principal component (Z PC–1o) was extracted. The first calculated principal component was modeled through a linear regression of Z PC–1c (dependent variable) on PDO–AMO (independent variables). The correlations between Z PC–1o vs Z PC–1c = 0.522 and the linear trend of Z PC–1c = 0.501, were significantly different from zero. This study contributes to addressing a research gap not otherwise explored to date in Sinaloa: modeling of the trend in MeD through aSPI–RDI and PDO–AMO. The model can be used to help schedule agricultural irrigation at the most productive times.