Teleconnection patterns play a crucial role in modulating large-scale atmospheric circulation and significantly influence regional climatic variables, including snow cover dynamics. This study aims to examine the impacts of 29 regional and extra-regional teleconnection indices on the spatial and temporal variability of snow cover in the Maroon River basin, a key water-supplying region in southwestern Iran that has experienced severe hydrological extremes in recent decades. Daily snow cover data derived from the MODIS sensor onboard the TERRA satellite for the period 2001–2022 were processed using the Google Earth Engine platform. Snow-covered areas were identified using the Normalized Difference Snow Index (NDSI) with a threshold of 0.4, and the data were aggregated at monthly and cold-season scales. Teleconnection indices—including ENSO-related indices, NAO, PDO, TSA, TNA, EPO, SCA, AAO, and SOLAR—were obtained from the NOAA database. The relationships between snow cover variability and teleconnection patterns were quantified using Pearson correlation analysis, considering both simultaneous and lagged effects.The results indicate that indices such as TSA, EPO, TNA, ESPI, PBO, and OSI exhibit strong negative correlations with snow cover, reflecting suppressed snow accumulation during their positive phases. In contrast, PDO, PNA, MEI.v2, and several Niño indices show significant positive correlations, particularly during the cold season, while the SCA pattern enhances snow cover during spring. Overall, snow cover variability in the Maroon Basin is governed by the combined influence of ocean–atmosphere oscillations and solar activity, with the strongest responses occurring in cold and transitional seasons.These findings highlight the value of integrating teleconnection-based climate signals into snowpack and hydrological forecasting, providing a practical framework for improving water resource management and climate risk assessment in mountainous regions.