Prediction of climatic variables, in particular those related to wind and solar radiation, has developed a huge interest in recent years, mainly due to its applications to renewable energy. In many cases there is a large number of factors that influence the climatic variable of interest, and the researcher chooses the most relevant ones (based on previous knowledge of the region, availability, etc.) and runs a series of experiments combining the available data in order to find the combination that provides the best prediction. In this work we present two applications of Niching Genetic Algorithms to solve the problem of selection of variables for the estimation of Solar Radiation. On one hand, this methodology is able to estimate a given climatic variable using databases with missing data, since the algorithm can compensate it by the use of others. On the other hand, we present a methodology that allows us to select the relevant input variables for a given climatic variable estimation or prediction problem, in a systematic way, using the same Genetic Algorithm with different parameters. Both methods were tested in the estimation of daily Global Solar Radiation in El Colmenar (Tucuman, Argentina), using linear regression on data from 14 weather stations spread along the north of Argentina. The results obtained show that the methodology is appropriate, providing an RMSE = 236 [MJ/m(2)] and R = 0.926 using an average of 64 out of 329 initial variables, on a 70 individuals/85 generations combination. For a 200 individuals/150 generations combination it obtained an RMSE = 2.34 [MJ/m(2)] and R = 0.928 using an average of 54 variables. (C) 2012 Elsevier Ltd. All rights reserved.