In any agricultural system climate plays a fundamental role setting the atmospheric scenario where crops grow and develop, capturing resources and transforming them into marketable products. For this reason variations in climate can produce drastic changes in the production output so any attempt to study and even reduce the effects of climate variability on crop yields is highly justified.

Precipitation in Chile is identified as one of the most important meteorological variables for agricultural productivity not only because it represents the direct supply of water for rain fed agriculture but also because its amount and distribution in winter determines the amount of water available for irrigation when snow melts during spring and summer. In addition to that Chile shows a significant ENSO climatic footprint, so the possibility of using that information to study the effects of climatic variability and to explore alternatives reducing its negative consequences is encouraging.

Although simple empirical relationships can be built using the amount of precipitation as a predictor variable, a more comprehensive study on the effects of climate variability on agricultural crops must include all the meteorological variables that determine biomass accumulation. However such records are not always available, or their lengths may be insufficient to study the effects of climate variability on crop yields.

To solve this problem stochastic synthetic series can be generated by using random number generators whose outputs have the property of reproducing the main statistical characteristics of the weather series from which their parameters were fit. Those algorithms are known as weather generators and have gained popularity among scientists to address statistical questions in those situations where sufficient meteorological records are not available.

The purpose of this study is to characterize the variability on wheat yields as well as the main hydrological features of Chile’s central Valley, by using daily weather generators conditioned on El Niño phases and crop simulation models. This study is a first step towards exploration of the value of climate forecasts and the development of management strategies to take advantage of them.