<p>This study investigates the spatiotemporal dynamics of rainfall and streamflow in Ceará, Brazil, using sample entropy and multiscale entropy methodologies. The analysis aims to elucidate the complexity and predictability of these hydrological variables across various temporal scales. Both original and deseasonalized series were analyzed, revealing distinct spatial and temporal patterns of entropy. Streamflow series exhibit greater regularity and predictability compared to rainfall. A 10-year moving window trend analysis indicates a decrease in rainfall complexity in coastal regions, suggesting more predictable patterns over time. Streamflow complexity trends align with these findings. Additionally, Normalized Mutual Information analysis highlights the dependency between rainfall and streamflow across stations, providing insights into their hydrological relationships and the influence of geographical and climatic factors. This research emphasizes the importance of multiscale entropy-based analyses for understanding hydrological complexity and informing water resource management in changing climatic conditions.</p>