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Sea surface temperature is the key variable when tackling seasonal to decadal climate forecasts. Dynamical models are unable to properly reproduce tropical climate variability, introducing biases that prevent a skillful predictability. Statistical methodologies emerge as an alternative to improve the predictability and reduce these biases. In addition, recent studies have put forward the non-stationary behavior of the teleconnections between tropical oceans, showing how the same tropical mode has different impacts depending on the considered sequence of decades. To improve the predictability and investigate possible teleconnections, the sea surface temperature based statistical seasonal foreCAST model (S^4CAST) introduces the novelty of considering the non-stationary links between the predictor and predictand fields. This paper describes the development of the S^4CAST model whose operation is focused on studying the impacts of sea surface temperature on any climate-related variable. Two applications focused on analyzing the predictability of different climatic events have been implemented as benchmark examples

S&lt;sup&gt;4&lt;/sup&gt;CAST v2.0: sea surface temperature based statistical seasonal forecast model

S<sup>4</sup>CAST v2.0: sea surface temperature based statistical seasonal forecast model