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Spatio-temporal variation of phytoplankton biomass were analyzed for five typical regions in the Yellow Sea (YS) during 2003–2018, using monthly long-term satellite data. The correlation between phytoplankton biomass and physical oceanographic parameters were explored using multiwavelet coherence for each region. The results showed the phytoplankton biomass decreased from the inshore to the center of the YS. There were opposing trends observed for phytoplankton biomass in the coastal area and the middle of the YS. The phytoplankton biomass varied seasonally for five the different regions due to the influence of sea surface temperature (SST), photosynthetically active radiation (PAR), and mix layer depth (MLD). The highest and lowest phytoplankton biomass occurred in April and July in most of regions. The phytoplankton biomass increased in the spring and decreased in winter for all five regions during 2003–2018. The SST, PAR and MLD also increased in most regions during the study periods. The Chlorophyll- a (Chl- a) had an interannual periodicity, mostly influenced by the MLD among the physical oceanographic parameters explored. The Chl- a showed a negative correlation with the SST and the PAR, and a positive correlation with the MLD. Among the physical oceanographic parameters explored, the PAR and the MLD were the key factors that influenced the variation of phytoplankton biomass in the estuary of the Yangtze River (EYR) and the North Yellow Sea (NYS), while the SST and the PAR were the main factors in the Jiangsu Shoal (JSS). In the Yellow Sea Cold Water Mass (YSCWM) and the south coast of Shandong Peninsula (SCSDP), the SST and the MLD were the main factors influencing for the phytoplankton biomass among the physical oceanographic parameters explored, respectively. The best three-factor combination all included Niño 3.4 in each region, which suggest that El Niño had a certain influence on the variation of the Chl- a in the YS over a long time scale. • Phytoplankton biomass in the Yellow Sea was inversely correlated with SST and PAR, and positively correlated with MLD. • MWC can characterize the temporal-scale relationships between phytoplankton biomass and oceanographic parameters. • Combinations of factors that best explained phytoplankton variations were different among regions in the YS. [ABSTRACT FROM AUTHOR]