## 1 Introduction  
The impacts of climate change on the hydrological cycle and water budgets have been extensively studied, and the findings highlight significant changes from global to regional scales [1; 2]. Changes in temperature and precipitation caused by climate change can affect the flow and transport of sediment in watersheds [3]. These changes in streamflow can affect water availability, which can then impact irrigation, urban water supply, and hydropower production [4; 5]. Additionally, changes in sediment load can affect river geomorphology, river ecosystems, and reservoir capacity [6]. According to the Intergovernmental Panel on Climate Change (IPCC), worldwide average precipitation and temperature at the surface, floods, and droughs have changed significantly and are expected to continue to do so [7]. Developing countries, such as India, are especially susceptible to the consequences of climate change on agriculture and water [8; 9; 10]. Climate change is also affecting soil types, as soil erosion and sediment yield are controlled by rainfall and runoff [11; 12; 13]. Overall, climate change is altering the hydrological process by changing rainfall patterns and the timing and magnitude of streamflow. In recent years, the importance of streamflow forecasting has gained significant recognition [14; 15]. On the other hand, streamflow prediction plays a vital role in long-term water resources planning and management [16; 17; 18]. Streamflow predictions are based on climate models and scenarios, which provide insights into potential changes in streamflow patterns under different climate change scenarios [11; 19; 20]. There have been many studies on the consequences of climate change using Global Circulation Models (GCMs) [21; 22; 23]. Performing the ensemble mean of multiple GCMs blindly may account for the uncertainty caused by the presence of poor model performance. Furthermore, consideration of all models is thought to be constrained by time and computational resources. Alternatively, some climate change studies used different statistical metrics (root-mean-square error, correlation coefficients, standard deviation, skill scores, etc.) for model selection [24; 25]. Understanding the flow of water and sediment in streams is essential for predicting the consequences of climate change on the environment. Researchers have used various methods, including empirical, statistical, and simulation techniques, to study these impacts. One model that has proven to be reliable in these analyses is the SWAT model, which is able to accurately assess hydrological and environmental changes in different land types and climate conditions [26; 27; 28]. It also includes output components that allow for the analysis of various water-related systems in watersheds. Recent studies have used the SWAT model to evaluate the consequences of changing the climate on streamflow and sediment load [22; 29; 30]. For example, [PERSON] et al. (2023) [18] used the SWAT model to evaluate the impact of climate change and land use change on streamflow over the Parvara Mula Basin, India. The findings of the study indicate an increase in streamflow for future periods. [PERSON] et al. (2021) [31] used the model to study the upstream region of the Mekong Basin, finding that both temperature and precipitation were projected to increase, while changes in sediment load were inconsistent. [PERSON] and [PERSON] (2019) [32] identified critical sub-watersheds in the Betwa Basin, India that were most vulnerable to sediment yield under both current and future climate conditions using SWAT model. This information can be used to prioritize and manage these areas for better water resource management. Several studies have been conducted in recent years to better comprehend the consequences of changing climate on streamflow and sediment in various watersheds around the world. [PERSON] et al. (2016) [33] assessed the consequences of climate change on streamflow and sediment yield in the Gorganroud watershed, Iran using the SWAT model.