The　furnace　temperature　is　one　of　the　most　critical　controlled　variables　in　the　municipal　solid　waste　incineration　(MSWI)　process.　The　primary　challenge　of　intelligent　optimal　control　is　to　construct　a　high-precision　and　interpretable　controlled　object　model　in　terms　of　furnace　temperature.　To　address　this　problem,　this　article　proposes　a　novel　modeling　method,　i.e.,　linear　regression　decision　tree　(LRDT),　to　construct　furnace　temperature　with　airflow　and　grate　speed　as　the　inputs.　LRDT　model　consists　of　(T/2-1)　intermediate　nodes　and　T　leaves.　The　intermediate　nodes　are　specified　by　the　mean　square　error　for　developing　the　tree-　based　model　structure.　These　leaves　provide　the　predicted　output　by　operating　the　Tikhonov　least　square　method,　which　can　boost　the　prediction　performance.　Moreover,　LRDT　uses　leaf　prediction　under　a　unique　path　as　the　final　output,　which　improves　the　interpretation　of　the　LRDT-based　furnace　temperature　model.　Finally,　the　proposed　method　is　verified　by　using　actual　MSWI　process　data,　and　the　interpretability　of　the　model　is　analyzed　in　detail.