In Mexico, arid regions cover more than half of the territory, with desertification, degradation and droughts being the main problems in these ecosystems, caused mainly by climatic events and anthropogenic activities. The objective of this research was to apply multicriteria assessment (MCE) techniques to model factors that favor the increase in aridity, and to be able to identify areas with different levels of aridity in the Northwest region of Mexico by 2023. The Analytical Hierarchy Method (AHP) and Weighted Linear Summation (WLC) were used in a Geographic Information Systems (GIS) environment. The modeling was carried out with satellite images, which were: Temperature, Soil Moisture, Precipitation, Slopes, Normalized Differential Vegetation Index (NDVI), Digital Elevation Model (DEM), Vegetation Cover and Evapotranspiration. The results show that thanks to the effective integration of the factors with the EMC technique, regions with different levels of aridity could be identified; the northern and northwestern regions have a greater arid surface area with 41%, while the semi-arid regions, located in the west, represent 30% of the surface area, while the sub-humid regions were located in the southeast with 27% of the surface area.

Aridity is a key climatic factor influencing ecosystem dynamics and land degradation in arid and semi-arid regions. This study analyzes the spatial relationship between aridity and land cover degradation in northwestern Mexico during 2005–2020 using a Geographically Weighted Regression (GWR) model, complemented by spatial autocorrelation techniques including Moran’s I and Local Indicators of Spatial Association (LISA). Aridity was derived from climatic data, and land cover transitions were used as proxies for degradation. The results indicate that the study area is predominantly characterized by arid and semi-arid conditions, where degradation-related transitions are strongly concentrated. In particular, transitions from shrubland to grassland (59.53%) and from shrubland to bare soil (93.60%) occur primarily under arid conditions, highlighting the high vulnerability of these ecosystems to water deficit. The GWR model explains approximately 49.5% of the spatial variability in degradation. However, residual analysis shows strong spatial autocorrelation (Moran’s I = 0.72, p < 0.001), indicating spatially structured patterns not fully captured by the model. These findings demonstrate that, although aridity is a key driver, additional factors influence degradation patterns.