Changing irrigation water demand (IWD) and supply (IWS) patterns (size and time) under increased climate variability and socio-economic development is significantly effecting the water and food production in the densely populated South Asia (SA). Considering food security paradigm of SA, where rice and wheat are major staple and water-intensive crops, this study aims to investigate the linkages in IWD by crops and IWS by sources (surface and groundwater) using integrated climate and socio-economic projections. The novel aspect of this study is to explore IWD and IWS pattern shifts during critical crop growth stages (CW’s), which is previously less studied with no remarkable research evidence for IGB region. Quantification of shifts in IWD and IWS patterns in future is crucial for long-term integrated water resources and agricultural planning. For this, LPJmL crop-water model is forced with an ensemble of eight state of the art downscaled GCM at 5 arc-min resolution. To assess the combined impacts of climate and socio-economic changes, RCP-SSP framework is used. Our statistical analysis results show that IWD is higher in vegetative stage (CW1) than the reproductive stage (CW2) during both Rabi and Kharif cropping seasons. Water demand is decreasing in future for wheat while increasing for rice. IWS is decreasing substantially from surface while increasing largely from groundwater resources during Rabi. Though, IWS during kharif season is increasing largely from both surface and groundwater resources. There is mismatch in demand and supply as evident from the results suggesting 10 days early wheat planting reduces IWD by 8.0% in F1, 18.7% in F2 and 28.4% in F3 during CW1 with a decrease of 7%, 30 % and 62.56% during F1, F2 and F3 in CW2. Increased IWS with larger contribution from groundwater resources is projected for both crops in future. Water gap between demand and supply during both CW’s in future is increasing for Rabi and Kharif suggesting 10 days early planting of wheat while 20 days delay in kharif planting. Estimation of IWS by sources helped in assessing shifts in percent (%) dependency of water supply from different sources. Moreover, Spatio-temporal mismatch between water demand and supply help exploring geospatially driven water gap trends consequently, highlighting water stress hotspots during CW’s in future.