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The volatile behavior of solar energy is the biggest challenge in its successful integration with existing grid systems. Accurate global horizontal irradiance (GHI) forecasting can resolve this issue and lead to early and effective participation in the energy market. This study proposes a new hybrid deep learning model, namely long short term memory–convolutional neural network (LSTM–CNN), for hourly GHI forecasting, which models the spatio-temporal features by integrating the long short term memory (LSTM) and convolutional neural network (CNN) model. The proposed model is trained with the meteorological data of 23 locations of California State, USA, which includes temperature, precipitation, relative humidity, cloud cover, etc., as input parameters. The proposed hybrid LSTM–CNN model firstly uses LSTM to extract the temporal features from time-series solar irradiance data, followed by CNN, which extracts the spatial features from the correlation matrix of several meteorological variables of target and its neighbor location. The prediction accuracy of the developed model is analyzed rigorously by examining the performance for a year, for four seasons and under three sky conditions. Besides, the proposed LSTM–CNN model shows a forecast skill score in a range of about 37%–45% over few standalone models, including smart persistence, support vector machine, artificial neural network, LSTM, CNN and other hybrid models. The findings of the present work suggest that the proposed hybrid LSTM–CNN model is a reliable alternative for short-term GHI prediction due to its high predictive accuracy under diverse climatic, seasonal and sky conditions. • A hybrid deep learning model, namely LSTM–CNN for hourly GHI forecasting is proposed. • LSTM–CNN extracts the spatio-temporal features from the dataset. • Model is trained with meteorological data of 23 locations of California State, USA. • Performance is evaluated for a year, four seasons and under three sky conditions. • The proposed model shows forecast skill score in a range of about 37%–45%. [ABSTRACT FROM AUTHOR]