This study examines the spatio-temporal evolution of precipitation in the State of Guanajuato, Mexico, from 1981 to 2016 by analyzing monthly series from 65 meteorological stations. A rigorous data quality protocol was implemented, selecting stations with more than 30 years of continuous data and less than 10% missing values. Multiple Imputation by Chained Equations (MICE) with Predictive Mean Matching was applied to handle missing data, preserving the statistical properties of the time series as validated by Kolmogorov-Smirnov tests (p=1.000 for all stations). Homogeneity was assessed using Pettitt, SNHT, Buishand, and von Neumann tests, classifying 60 stations (93.8%) as useful, 3 (4.7%) as doubtful, and 2 (3.1%) as suspicious for monthly analysis. Breakpoints were predominantly clustered around periods of instrumental changes (2000-2003 and 2011-2014), underscoring the necessity of homogenization prior to trend analysis. The Trend-Free Pre-Whitening Mann-Kendall (TFPW-MK) test was applied to account for significant first-order autocorrelation (rho 1 > 0.3) present in all series. The analysis revealed no statistically significant monotonic trends in monthly precipitation at any of the 65 stations (alpha=0.05). While 75.4% of the stations showed slight non-significant increasing tendencies (Kendall's tau range: 0.0016 to 0.0520) and 24.6% showed non-significant decreasing tendencies (tau range: -0.0377 to -0.0008), Sen's slope estimates were negligible (range: -0.0029 to 0.0111 mm/year) and statistically indistinguishable from zero. No discernible spatial patterns or correlation between trend magnitude and altitude (rho=-0.022, p>0.05) were found, indicating region-wide precipitation stability during the study period. The integration of advanced imputation, multi-test homogenization, and robust trend detection provides a comprehensive framework for hydroclimatic analysis in semi-arid regions. These findings suggest that Guanajuato's severe water crisis cannot be attributed to declining precipitation but rather to anthropogenic factors, primarily unsustainable groundwater extraction for agriculture.