Climate change refers to a significant and sustained alteration in the average weather data over a specific period of time. This time period is typically ten years or longer and involves changes in the mean climatic conditions or statistically significant changes in the distribution of weather phenomena. One of the climate elements affected by climate change is precipitation. Precipitation is the third factor in the differences of Iran's climate and is the most fluctuating climatic element. Even slight changes in the type (solid and liquid precipitation) and amount of precipitation can disrupt the natural environmental balance. Due to the importance of precipitation, understanding it is crucial and has implications for the management of water resources, agriculture, and other systems. To understand the effects of climate change in the future, research institutions worldwide have simulated the global climate using Global Climate Models (GCMs). GCMs are extensively used to assess the impacts of global warming on weather, climate, and the water cycle. However, the lower resolution of these models hinders the detection of variable features such as precipitation, and using them introduces uncertainties, often showing significant deviations compared to observed data. On the other hand, higher-resolution climate data is needed to accurately evaluate the effects of climate change. Therefore, to address the issue of the large-scale nature of climate model data, statistical correction methods (bias correction) or downscaling techniques are used. Over the past decades, several research groups and international collaborations, including the Intergovernmental Panel on Climate Change (IPCC), have provided sets of predicted data on past and future global climate conditions using global climate models. Simulations from global climate models are archived by the Coupled Model Intercomparison Project (CMIP), which is one of the most important resources for studying 21st-century climate conditions. Recently, a new version of reports on climate models has been released, which represents an advancement in physical processes and higher spatial resolution compared to previous reports. This serves as a scientific basis for evaluating past and future changes in climate conditions. In this study, the daily precipitation outlook of the northwest region of Iran for the period 2021-2100 was investigated based on the sixth phase of the Coupled Model Intercomparison Project (CMIP6) under four scenarios (SSP 1-2.6, SSP 2-4.5, SSP 3-7, SSP 5-8.5). Among the 26 models studied, the MPI-ESM-1-2LR model was selected as the preferred model for predicting the region's daily precipitation. This model is the latest version of the Max Planck Institute's Earth System Model, available in both high and low resolutions, and is the fundamental basis for CMIP6 models and the prediction of climate variables at seasonal and decadal scales. It is generated with a horizontal resolution of 1.9˚*1.9˚ (approximately 250 square kilometers). Due to the large scale of the model data, the data were first bias-corrected using the delta change method, and then the precipitation characteristics of the region were analyzed in terms of percentage change compared to the baseline period, precipitation trends in the future period, and precipitation variations in each month under the four scenarios. The results showed that the precipitation outlook of the region under different scenarios will have both decreasing and increasing values. The highest percentage of change and decreasing trend was observed under the SSP 3-7 scenario (12% decrease compared to the baseline period), which is more prominent in the southwest part of the region. On the contrary, the highest increase in precipitation values was observed under the SSP 1-2.6 scenario, showing an average increase of over 6% in precipitation compared to the baseline period, especially in the central parts of the region. The highest decreasing and increasing trends under all four scenarios were observed in the Sardasht and Tabriz stations, respectively. Another important finding of this study is the increase in heavy precipitation events and their displacement towards the warm months of the year, which will cause uneven distribution of precipitation in limited days. From a seasonal perspective, the predicted precipitation changes in the model compared to the baseline period showed decreasing trends in spring and winter and increasing trends in summer and autumn. The interpolated maps and diagrams showed that although climate change is a global phenomenon, the studied region behaves differently in terms of precipitation changes due to the differences in climate in each part of it.