Characterization and knowledge of the variability of atmospheric dynamics on a small scale in the city of Riobamba, Ecuador, are achieved through the chaos theory. Meteorological data is taken every hour during four years, including variables such as wind speed, wind direction, incident radiation, temperature, and humidity, from the ESPOCH, SAN JUAN, and QUIMIAG weather stations in the canton of Riobamba. The van Ulden and Hostlang models are used to calculate the Obukhov length, surface heat fluxes, and latent heat flux. The chaos theory is applied to study the variation of atmospheric microdynamics. The Lyapunov coefficients, Kolmogorov-Sinai entropy, and Kaplan-Yorke fractal dimension are determined. Before analysis, noise reduction is necessary due to the lack of correlation, especially in the Obukhov length. This research follows a longitudinal design and employs quantitative and explanatory methods based on data analysis, statistical-mathematical techniques, and inductive-deductive approaches. The results indicate a highly variable system, reflected in a high number of Lyapunov coefficients, fractional dimensions, and entropy variations. The microdynamic parameters exhibit hyperchaotic behavior, as indicated by the presence of more than one positive Lyapunov coefficient. The variables also demonstrate a fractional fractal dimension, highlighting the irregularity in the geometric representation of the system.

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.