•WCA-BME can be used to determine quantitatively the SSCC-SST association.•Strong synchronous SSCC and SST variations occurred at 1 and 5-year periods.•Strong association detected in mid-latitude regions of Pacific and Atlantic Ocean. Sea surface temperature (SST) can influence the phytoplankton biomass, measured as sea surface chlorophyll concentration (SSCC), by affecting the physical and chemical properties of the seawater, living environment, and the consumption of zooplankton in a complex way. Yet, the quantitative assessment of the spatiotemporal variation of the inherent synchronous association between SSCC and SST at large spatial and temporal scales is still lacking. Accordingly, in the present study a synthetic approach was proposed that combines wavelet coherency analysis (WCA) with Bayesian maximum entropy (BME) modeling and hotspot analysis in order to evaluate the association between SSCC and SST globally during the period July 2002-February 2019. The WCA-based statistical results showed that SSCC has strong association with SST; particularly strong synchronous variations between SSCC and SST were found at the 1-year and the 5-year periods. During the 1-year period, cluster characteristics were explored in the BME-generated space–time maps of the association strength as well as in the corresponding hotspot maps. Geographically, high association strengths between SSCC and SST were detected in the mid-latitude regions of the Pacific Ocean, in the south and north of the tropical regions of the Atlantic Ocean, and in the southern part of the Indian Ocean. Temporally, most of the sub-regions exhibited a stable level of association strength during the entire study period (only a few sub-regions exhibited fluctuations or a slightly decreasing association strength trend). In conclusion, by assimilating the available knowledge bases at each grid point the proposed synthetic approach assessed quantitatively the strength of the periodic association between SSCC and SST globally; and the approach could be employed to map the space–time variation of the association strength between related natural attributes in the space–time-frequency domain.