Hydrologic projections of current regional climate models are uncertain over mountainous regions because of the poor representation of complex terrains. Here, we use multiple AI models to generate observation-constrained projections of the occurrence probability of record events (REs) of annual maximum and minimum daily streamflow (Qmax and Qmin) in Pakistan. First, we trained and tested the AI models, using the occurrence probability of REs from simulated (input) data from five regional climate models and observed (output) river discharge data from 1962–2022. Then, we use the trained AI models to project the observation-constrained occurrence probability of REs until 2099 and conduct principal component analysis for the robust selection of AI model ensembles. The observation-constrained projections detect more REs in Qmax and Qmin in the late 21st century than expected under the stationary system does, highlighting intensifying hydroclimatic extremes. The upper Indus River shows that the return period of REs in Qmax and Qmin is approximately 15 years. The Chenab and Kabul Rivers (Jehlum River) are prone to more REs in Qmax (Qmin), with a return period of approximately 11 years. Uncertainties in the RE occurrence probability projections from regional climate models can be attributed to the heterogeneity in hydroclimatic and physiographic characteristics across the study basins. This study suggests the nonstationarity of four major river basins in Pakistan, where water resources management should be updated with basin-specific strategic plans.

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