Optimization in irrigation scheduling using weather forecast has been proven to achieve better productivity along with reduced irrigation water requirements. We developed a farm-scale hydrological model coupled with a chance-constraint optimization to take short to medium range weather forecast and prescribe the optimal irrigation amount determined by developing the conditional probability density functions of the rainfall and subsequently the soil moisture for the days in forecast range. The stress-avoidance was ensured by maintaining the probability of crops undergoing water stress is less than a prescribed threshold (reliability factor, α). The framework was implemented for irrigation decision simulation at extended range by downscaling the forecast with Nonhomogeneous Hidden Markov Model (NHMM) as an input and produce irrigation decision in extended range (15 to 30 days). The optimization framework ensured minimal water use without significant crop water stress. The method was tested at two site locations in Nashik district in the state of Maharashtra, both being involved in grape cultivation (referred herein as Site 1 and Site 2). In short-to-medium range weather scale, the model was implemented with varied α (0.5 to 0.95) and interval between two subsequent irrigation application (1, 3 and 7 days) and significant amount of water savings with respect to the farmer’s applied irrigation could be achieved. The simulation-optimization framework was only tested with α=0.95 and once in 7 days irrigation application for extended range, and yet no significant detrimental effect on yield was observed whereas in kharif season significant potential of water savings was observed both in Site 1 and 2. While the framework in short to medium range is useful for optimal real time irrigation decision making, in the extended range, it can be implemented in planning of irrigation for the upcoming month to avoid the inconvenience of instant arrangement of water, especially in case of drought-hit regions. Considering that irrigation accounts for over 80% of the total water use worldwide, the value of such an approach as a decision-support tool for irrigation optimization is self-evident.