Precipitation is one of the most important factors affecting the climate, hydrological processes and living environment. Hence, the precipitation forecast is significant for water resource exploitation and preparation in extreme climatic events such as drought and floods. In this context, teleconnection indices are commonly used as predictors across the globe. However, most studies have focused on investigating the correlation between seasonal precipitation and teleconnection indices from the meteorological stations by using some limited models to simulate the precipitation. Machine Learning models (MLMs) are widely applied to solve hydrological problems including rainfall forecasting. The important of this modelling is that the skill of the software to plot the input-output patterns without aforementioned expertise of the factors affecting the forecast parameters (Najah et al., 2011; Hipni et al., 2013; Ridwan et a., 2021). Data-driven prediction models using MLMs are capable tools to develop various models with minimal inputs. MLMs are a field of artificial intelligence (AI) used to induce regularities and patterns, providing easier implementation with low computation cost, as well as fast training, validation, testing, and evaluation, with high performance compared to physical models, and relatively less complexity (Makanik et al., 2013). The continuous advancement of MLMs over the last two decades confirmed their suitability for precipitation forecasting with an acceptable rate of outperforming conventional approaches (Mosavi et. Al., 2017; Mosavi et al., 2018). This study evaluated the use of 40 teleconnection indices by exploiting 4 machine learning models namely Generalized Regression Neural Network (GRNN), Multi-Layer Perceptron (MLP), Least Squares Support Vector Machine (LSSVM), and Multi Linear Regression (MLR) to forecast and model seasonal precipitation in a larger scale than meteorological stations, specifically main basins, and sub-basins of Iran. For t
