Precipitation Regime Variability in the Upper Pastaza River Basin, Ecuador: Evidence of Recent Changes from CHIRPS-Based Clustering Analysis

Precipitation variability in the tropical Andes is strongly influenced by complex topography and regional hydroclimatic processes, yet the spatial organization and temporal evolution of precipitation regimes remain insufficiently characterized. This study identifies dominant precipitation regimes in the Upper Pastaza River Basin, Ecuador, and examines their variability during 1981–2025 using daily precipitation data from the Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) dataset. CHIRPS estimates were evaluated against observations from three meteorological stations, revealing spatial differences in precipitation estimation accuracy and a general underestimation of monthly precipitation totals. Empirical Orthogonal Function (EOF) analysis and K-means clustering were applied to identify four recurrent precipitation Weather Types (WTs), ranging from a dominant low-intensity regime (WT1), representing approximately 70% of classified days, to a relatively infrequent but intense precipitation regime (WT2), accounting for approximately 1.7%. Seasonal and interannual variability was characterized through regime occurrence frequencies and temporal recurrence patterns. Monotonic trends were assessed using Kendall’s rank correlation coefficient and Sen’s slope estimator, while the burstiness index was used to examine temporal irregularity. A statistically significant positive trend was identified in WT2 occurrence (Kendall’s τ = 0.3830, p = 0.0002), whereas no significant trends were detected for the remaining regimes. Comparison with the recent period (2015–2025), using a consistent classification framework, revealed differences in regime occurrence while preserving broadly similar spatial precipitation patterns. These findings highlight changes in the occurrence of specific precipitation states rather than a generalized redistribution of all regimes, providing insights into hydroclimatic variability across the Andes–Amazon transition zone.