Fig. \ref{fig:individual} shows the East African rainfall annual cycles of CMIP5 individual models for the AMIP and historical runs as compared to the GPCC result. For most models, the historical runs (red lines) are wetter than the AMIP runs (blue lines) during OND while they are drier during MAM especially April, consistent to the multimodel mean historical$-$AMIP results in Fig. \ref{fig:boxplotDif}. For the AMIP run, some models  are highly correlated with the GPCC (e.g. MRI-CGCM3, IPSL-CM5A-LR, FGOALS-g2 and CSIRO-Mk3-6-0) while some models are not (e.g. inmcm4, BNU-ESM and NorESM1-M). The correlation coefficients with the GPCC, as well as the root mean squared errors (RMSE) compared to the GPCC,  for all the CMIP5  AMIP and historical runs are shown in the scatter plots of Fig. \ref{fig:scatter}a and b.  There are five AMIP models with a correlation coefficient greater than 0.8 and a RMSE less than 0.5 mm day$^{-1}$, namely MRI-CGCM3, IPSL-CM5A-LR, IPSL-CM5A-MR, CMCC-CM and MPI-ESM-MR (Fig. \ref{fig:scatter}a). Moving from AMIP to historical runs (Fig. \ref{fig:scatter}b), all of them have a higher RMSE and four of them (except MPI-ESM-MR) have a less correlation coefficient.  Of all these 21 models, MRI-CGCM3 is of particular interest. Its AMIP run reproduces the GPCC annual cycle very well but when coupled, it dramatically overestimates the OND rainfall and underestimates the MAM rainfall, similar to the coupled multimodel mean bias. Therefore, MRI-CGCM3 serves as an ideal model to study the bias induced by turning from SST-forced GCMs into atmosphere-ocean coupled GCMs.