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Abstract Douglas-fir (Pseudotsuga menziesii) is native to western North America. It grows in a wide range of environmental conditions and is an important timber tree. Although there are several studies on the gene expression responses of Douglas-fir to abiotic cues, the absence of high-quality transcriptome and genome data is a barrier to further investigation. Like for most conifers, the available transcriptome and genome reference dataset for Douglas-fir remains fragmented and requires refinement. We aimed to generate a highly accurate, and complete reference transcriptome and genome annotation. We deep-sequenced the transcriptome of Douglas-fir needles from seedlings that were grown under nonstress control conditions or a combination of heat and drought stress conditions using long-read (LR) and short-read (SR) sequencing platforms. We used 2 computational approaches, namely de novo and genome-guided LR transcriptome assembly. Using the LR de novo assembly, we identified 1.3X more high-quality transcripts, 1.85X more “complete” genes, and 2.7X more functionally annotated genes compared to the genome-guided assembly approach. We predicted 666 long noncoding RNAs and 12,778 unique protein-coding transcripts including 2,016 putative transcription factors. We leveraged the LR de novo assembled transcriptome with paired-end SR and a published single-end SR transcriptome to generate an improved genome annotation. This was conducted with BRAKER2 and refined based on functional annotation, repetitive content, and transcriptome alignment. This high-quality genome annotation has 51,419 unique gene models derived from 322,631 initial predictions. Overall, our informatics approach provides a new reference Douglas-fir transcriptome assembly and genome annotation with considerably improved completeness and functional annotation.