Solar power is a critical source of renewable energy, offering significant potential to lower greenhouse gas emissions and mitigate climate change. However, the cloud induced-variability of solar radiation reaching the earth’s surface presents a challenge for integrating solar power into the grid (e.g., storage and backup management). The new generation of geostationary satellites such as GOES-16 has become an important data source for solar radiation forecasting at a large scale and high temporal frequency. However, there is no machine-learning-ready dataset that has integrated geostationary satellite data with fine-grained solar radiation information to support forecasting model development and benchmarking at a large geographic scale. We present SolarCube, a new ML-ready benchmark dataset for solar radiation forecasting. SolarCube covers 19 study areas distributed over multiple continents: North America, South America, Asia, and Oceania. The dataset supports short and long-term solar radiation forecasting at both point-level (i.e., specific locations of monitoring stations) and area-level, by processing and integrating data from multiple sources, including geostationary satellite images, physics-derived solar radiation, and ground station observations from different monitoring networks over the globe. We also evaluated a set of forecasting models for point- and image-based time-series data to develop performance benchmarks under different testing scenarios. The dataset and models are available at https://doi.org/10.5281/zenodo.11498739. The Python library to conveniently generate different variations of the dataset based on user needs is available at https://github.com/Ruohan-Li/SolarCube.