The issue of color inconsistency between images was caused by the differences in camera sensor spectral sensitivities and built-in image signal processing (ISP) modules. Two illumination-aware color consistency algorithms for cross-camera applications, namely the correlated-color-temperatures (CCT)-aware adaptive partitioning mapping algorithm and the category-aware adaptive classification mapping algorithm, were proposed to convert the RAW image from the color space of the source camera to that of the target camera. Specifically, by deriving the final mapping operator for the target scene based on the training operators obtained under typical light sources of different CCTs and types, specific mapping relationships were established for different lighting scenarios, thereby achieving the cross-camera image conversion. The performance of the algorithms was objectively evaluated using color difference metrics in the experiments. Results illustrated that both of the proposed algorithms could achieve better performance in terms of color difference and visual effect compared to the existing classical methods, effectively adapting to the diversity and complexity of lighting conditions, thereby exhibiting their potential for wide applications.