Anthropogenic climate change, driven by the escalating concentration of greenhouse gases, has confronted both natural and human systems with fundamental and unprecedented challenges in recent decades (IPCC, 2021). Among the most pronounced consequences of global warming are shifts in the frequency, intensity, spatial footprint, and timing of extreme hydro-meteorological events, particularly precipitation. As the moisture-holding capacity of the atmosphere expands in response to a warming of approximately 0.78°C, numerous studies caution that spatial and seasonal fluctuations within precipitation regimes will inevitably intensify. Addressing this critical challenge, the present study investigates precipitation dynamics across Iran during the historical period of 1980–2014. The primary methodological innovation lies in first evaluating the performance of individual downscaled NEX-GDDP-CMIP6 models, followed by the development of a non-linear, exponentially weighted multi-model ensemble. Fundamentally, this research seeks to demonstrate how formulating a non-linear ensemble framework that accounts for the spatial variance of modeling errors by assigning geographically dependent weights can enhance precipitation simulation accuracy. We also examine how individual models perform when benchmarked against this advanced approach. By rigorously assessing the capacity of standalone NEX-GDDP datasets alongside the proposed exponential ensemble (Exp-Ensemble), the findings reveal substantial errors in the individual models' ability to capture precipitation patterns reliably, particularly over complex mountainous terrains and coastal margins. When evaluated against GPCC observational datasets, these individual models exhibit a pervasive dry bias ranging from -25 to -50 mm, coupled with Root Mean Square Error (RMSE) values fluctuating between 127 and 155 mm. Notably, GFDL and EC-Earth3 display severe deviations in precipitation variance. Consequently, their spatial outputs across Iran's diverse topography are highly dispersed. Implementing the exponentially weighted multi-model ensemble strategy yielded a statistically significant reduction in these systematic errors. By calibrating grid-based weights according to the physical robustness of each model across distinct climate zones, the overall RMSE was driven down to 108 mm, while the dry bias was reduced to -23 mm. Despite these improvements, standard Bias-Correction Spatial Disaggregation (BCSD) algorithms continue to struggle. They fail to fully compensate for the missing micro-scale physics in model outputs along the southern coastlines of the Caspian Sea. Intense precipitation maxima exceeding 1000 mm within this narrow coastal strip are severely underestimated, peaking at a mere 600 mm even within the integrated ensemble framework. This glaring discrepancy highlights a computational deficit exceeding 50% for this specific geographic sub-region. Ultimately, the arid and semi-arid expanse of Iran represents a physical challenge for parameterizing precipitation schemes within the sixth phase of the Coupled Model Intercomparison Project (CMIP6). While it cannot eliminate the underlying negative bias ubiquitous across this latest generation of models, the weighted ensemble proposed herein successfully minimizes error variance. Integrating this methodology into broader climate impact evaluations and macro-level water resource policymaking effectively dampens structural uncertainties, securing a highly rigorous historical record for future hydrological projections in Iran and geographically analogous regions worldwide.