<p>Forecast errors can be reduced if observations are assimilated in regions with the largest percentage of analysis error growth. Adjoint or ensemble sensitivity analysis provides a way to determine the relationship between a scalar forecast metric and analysis/forecast state variables, whereby regions with the largest sensitivity can be identified. In this study, the regions where additional observations will impact the forecasts over the Indian subcontinent during the summer monsoon season have been identified using the ensemble approach. Five years of ensemble data drawn from the National Centers for Environmental Prediction (NCEP) The Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Global Ensemble (TIGGE) archive have been used to perform ensemble sensitivity analysis (ESA). Regions with the largest percentage of forecast cycles are determined for different forecast metrics over different domains. Results show that the climatological sensitivity fields for the Western Ghats and Bay of Bengal precipitation forecasts are most often sensitive to upstream regions, and more observations in these regions may improve the precipitation forecasts in the response regions. Additionally, the results indicate that over the Gangetic basin and Northeast region, precipitation forecasts are sensitive to analysis errors within the metric box region. Regions showing consistent sensitivity indicate that additional observations in these areas are likely to have the greatest impact on the forecast metric, offering optimal improvements to forecasts over the target domain.</p>