This research presents a comprehensive study focused on precipitation prediction for the Dead Sea region 

utilizing the Seasonal Autoregressive Integrated Moving Average (SARIMA) model. The investigation seeks to interpret 

the accuracy and reliability of the SARIMA model's predictions by comparing them with predictions derived from climate 

modeling techniques. The evaluation is based on key performance metrics, including Mean Squared Error (MSE), Mean 

Absolute Error (MAE), and Root Mean Squared Error (RMSE). Additionally, the paper examines the SARIMA model's 

predictive capabilities through a comparison with actual observations spanning the period from 2010 to 2022. The 

obtained results reveal an MSE of 12.84593, an MAE of 2.34407, and an RMSE of 3.584123 for this period. 

Significantly, the SARIMA model surpasses the predictions of prominent climate models (CMIP6), namely 

ACCESS_CM2, Earth3_Veg, GISS_E2, and HadGEM3, based on comparative performance assessments. The findings 

emphasize the robustness of the SARIMA model in capturing the essence of the observations and predicting 

precipitation patterns, not only through its superior performance against climate models but also through its alignment 

with actual observations. This study contributes to a deeper understanding of precipitation prediction in the Dead Sea 

region and underscores the potential of the SARIMA model in enhancing forecasting accuracy for hydrological and 

climatic investigations.