The Orbiting Carbon Observatory‐2 (OCO‐2) collects solar‐induced chlorophyll fluorescence (SIF) at high spatial resolution along orbits ( SIF¯oco2_orbit), but its discontinuous spatial coverage precludes its full potential for understanding the mechanistic SIF‐photosynthesis relationship. This study developed a spatially contiguous global OCO‐2 SIF product at 0.05° and 16‐day resolutions ( SIF¯oco2_005) using machine learning constrained by physiological understandings. This was achieved by stratifying biomes and times for training and predictions, which accounts for varying plant physiological properties in space and time. SIF¯oco2_005 accurately preserved the spatiotemporal variations of SIF¯oco2_orbit across the globe. Validation of SIF¯oco2_005 with Chlorophyll Fluorescence Imaging Spectrometer airborne measurements revealed striking consistency (R2 = 0.72; regression slope = 0.96). Further, without time and biome stratification, (1) SIF¯oco2_005 of croplands, deciduous temperate, and needleleaf forests would be underestimated during the peak season, (2) SIF¯oco2_005 of needleleaf forests would be overestimated during autumn, and (3) the capability of SIF¯oco2_005 to detect drought would be diminished. Plain Language Summary Newly available observations of solar‐induced chlorophyll fluorescence (SIF) from satellite sensors represent a major step toward quantifying photosynthesis globally in real time. However, existing satellite SIF records are restricted to low spatial resolutions, sparse data acquisition, or both. These limitations impede the full capability of SIF for improving our understanding of dynamics of photosynthesis and its response to environmental changes (particularly in heterogeneous landscapes) to better support carbon source/sink attribution and verification. This study developed a novel high‐resolution time series of spatially contiguous SIF for the globe, leveraging NASA's Orbiting Carbon Observatory‐2 measurements. We combined machine learning algorithms with known physiological constraints for this effort. Comparison with independent airborne SIF measurements revealed strong consistency, confirming the high quality of this new SIF data set. The high‐resolution and global contiguous coverage of this data set will greatly enhance the synergy between satellite SIF and photosynthesis measured on the ground at consistent spatial scales. Potential applications with this data set include advancing dynamic drought monitoring and mitigation, informing agricultural planning and yield estimation in a more spatially explicit way, and providing a benchmark for upcoming satellite missions with SIF capabilities at higher spatial resolutions. Key Points A spatially contiguous global OCO‐2 SIF data set at 0.05° and 16‐day resolutions ( SIF¯oco2_005) was developed using machine learning and physiological constraints SIF¯oco2_005 successfully preserves the spatiotemporal variability of the original OCO‐2 SIF retrievals, captures water stress, and reduces noise SIF¯oco2_005 is highly consistent with independent airborne measurements, demonstrating the effectiveness and validity of the prediction framework