Reference evapotranspiration (ET₀) is a climatic parameter and can be computed from weather data. It is one of the most important hydrological parameters for calculating crop water demand, scheduling irrigation systems, preparing input data to hydrological water-balance models, regional water resources assessment, and planning and management for a region and/or a basin. The climatic data from synoptic stations with more than 20 years continues record in West Azarbaijan province was used. The well-known FAO-PM56 method was used to calculate the ET₀. Then Multiple linear Regression (MLR) was used to estimate the ET₀. The RMSE, MEA, NSH, and R² were used to evaluate the performance of the MLR model. Then, the correlation coefficient (r) between ET₀ and each of the meteorological parameters was obtained. And finally, with using Path analysis method, the influence of direct (P) and indirect effects of the meteorological parameters on ET₀ was calculated. In the studied synoptic stations, NSH between 0.91 and 0.99,   R² between 0.91 and 0.99, RMSE between 0.05 and 0.15, and MEA between 0.04 and 0.12 were obtained. Also, it was found that the wind speed at all of stations had a significant correlation (at the 0.01% level) with ET₀. The path analysis results showed that the maximum value of P (direct effect of meteorological parameters on ET₀) in all of the stations belongs to wind speed. The P value of wind speed in Urmia equal to 0.85, Piranshahr equal to 0.99, Takab equal to 0.97, Khoy equal to 0.90, Sardasht equal to 1.06, and Mahabad equal to 0.78 are obtained.

 Assessing and predicting future climate change is of particular importance due to its adverse effects on water resources and the natural environment, as well as its environmental, economic and social effects. Meanwhile, rainfall is also an important climatic element that causes a lot of damage in excess conditions. West Azerbaijan Province is no exception. The aim of this study is to model and predict 30 years of rainfall in West Azerbaijan province. The statistical period studied is 32 years (2019-1987). Selected stations in the province include Urmia, Piranshahr, Takab, Khoy, Sardasht, Mahabad and Mako stations. Average slider time series models, Sarima (seasonal Arima), Health Winters were used for analysis and prediction and also linear regression and Mann-Kendall test were used to determine the data trend. The results show an increasing trend of precipitation in Urmia, Piranshahr, Khoy, Sardasht and Mako stations and a decreasing trend in Takab and Mahabad stations. According to the results of comparing the models used, the Health Winters model with the least error in the absolute mean of deviations, mean squared deviations and the percentage of absolute mean errors was introduced as the best precipitation forecasting model for West Azerbaijan province. province.                                     [A1] 

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