Accurate prediction of solar radiation is important for the design and optimization of photo-voltaic systems and for efficient energy management and sustainability planning. However, in many regions direct solar radiation measurements are scarce or not available, which makes the forecasting from available climatic variables more and more important. This study addresses this challenge by developing three simple-structured fuzzy logic models to predict daily global solar radiation using three different combinations of commonly recorded weather inputs: temperature and relative humidity, temperature and wind speed, and relative humidity and wind speed. The models were trained with data from Ankara, Türkiye and evaluated using mean absolute percentage error and root mean square error metrics. Comparing the models tested, the relative humidity and wind speed model resulted in the best forecasting performance with a mean absolute percentage error of 9.07% and a root mean square error of 0.85 kWh/m²-day. It also outperformed the feedforward artificial neural network model used for comparison, which produced an error of 14.38%. Results indicate that wind speed has more direct effects on estimation of solar radiation while the effect of temperature appears more indirect. A major contribution of the present study is that reasonably simple fuzzy logic models can provide good prediction performance if the right input variables are selected. This is particularly important when compared to more complex artificial intelligence methods, which usually require larger data sets and a larger number of input parameters. In this sense, the proposed approach also helps to overcome limitations reported in previous studies, such as the use of high-dimensional input sets and models developed mainly for short-term forecasting. Overall, the results show that fuzzy logic can be an accurate and computationally efficient alternative for solar radiation forecasting, especially when the available data are limited.