This research examines how well climate models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) simulate rainfall and temperature across various climate zones within the Euphrates River Basin at different durations. The study focuses on the area extending from the Birecik Dam in Turkey to the Haditha reservoirs in Iraq, which is characterized by geographic and climatic variation, which helps determine the ability of GCMs to estimate climate data at 1 degrees-pixel spatial resolution from 1982 to 2022. The study's findings indicate that rainfall and temperature estimates can be significantly affected by their geographical location or how they were measured when making a prediction about the weather. In comparison to other models, such as CMIP1 to 5, the performance of CMIP6 was relatively good for Temperature and had less bias, whereas the accuracy of CMIP6 for Precipitation had greater variation with respect to the various Climate Zones. In terms of humidity compared to aridity, there were significantly greater levels of Climate change uncertainty associated with the arid Climate Zones. The difference in model performance in the aspect of temporal accuracy indicates that most Models will have a tendency to underpredict precipitation and overpredict temperature in certain climate zones, while displaying higher consistency in others. While looking at the Taylor diagrams, we can observe that each model is suitable for providing a reliable representation of certain temperature models in specific areas/regions of the globe; therefore, not all Models are equally suited across all Climatic Regions. Examining how effectively various climate models represent normal weather patterns provides inconclusive findings about the accuracy of different models when compared against real-world historical records of weather data. For example, MRI-ESM2-0 and CNRM-CM6-1 appeared to accurately model the conditions of intermediate moist climates; however, their performance decreases significantly within dry regions. Conversely, HadGEM3-GC31-LL, BCC-CSM2-MR, and CanESM5 showed much stronger performance for dry regions-including semi-arid, arid, and even extremely arid conditions-than for humid/wet areas. This represents a high level of variability among the CMIP6 climate models across all climatic zones (including both land and ocean) and creates a significant amount of uncertainty with regard to how well any model will predict future climate changes. Because of this, it is also important to find ways of reducing these biases in order to improve how the climate models are used in future studies and development efforts.