Runoff efficiency (RE) represents the potential of a basin to generate runoff in response to precipitation and it varies based on climatology and physiography. It is a key metric that enables hydrologists to compare the hydrologic responses of basins across diverse climates and landscapes. In the large river basins of South Asia, RE plays a key role in floods and drought dynamics but has not been studied systematically. This study employs an integrated hydrologic-hydrodynamic modeling system, the Indian Land Data Assimilation System (ILDAS), to investigate the spatiotemporal variability of RE and their climatological and physiographic controls. The spatial variability of RE is mainly driven by mean precipitation and soil type. High RE is observed in the Central Indian basins, including Narmada, Brahmani, Godavari, and Mahanadi, due to high monsoon rainfall distribution and low permeability soils. However, large basins such as Brahmaputra, Ganga, and Subarnarekha exhibit low RE despite high monsoon rainfall, largely due to moderately permeable soil and significant baseflow contributions. Temporal variability in monthly RE influenced by event precipitation magnitude, antecedent soil moisture, and seasonal cycles, with the El Niño-Southern Oscillation (ENSO) exerting a notable influence at the interannual scale. La Niña events, particularly when coinciding with the monsoon season, enhance RE over the central eastern, central, and peninsular India, driven by ENSO’s amplified impact on runoff relative to precipitation. Conversely, during the winter season in northern India, ENSO effects on precipitation don’t translate into RE. Long-term trend analysis shows a rising trend in RE across several basins, during monsoon and pre-monsoon seasons, indicating an accelerated response of water cycle response linked to climate change. Notably, a positive trend in RE is observed in West Flowing Kachh-Sabarmati basins in the monsoon, pre-monsoon, and winter. Our findings enhance our understanding of the trends and drivers of RE and underscore the need for adaptive water management strategies in response to evolving patterns.