<p>Early warning systems play a crucial role in disaster risk reduction by enabling timely detection and response to natural hazards. However, the selection of appropriate technologies for early warning systems faces significant challenges, including uncertainty in expert evaluations, incomplete information expression, and subjective preferences that can compromise decision quality. To address these challenges in early warning system development, this study proposed an improved evidential linguistic CoCoSo method for technology selection in emergency contexts. The proposed improved evidential linguistic term set reduces computational complexity while better reflecting real-world decision-making scenarios encountered in early warning system implementation. Recognizing that experts’ risk attitudes significantly influence technology selection decisions, a risk coefficient is introduced to calibrate expert evaluations and minimize the impact of subjective preferences on early warning system design choices. To capture information conflicts among experts during the technology assessment process, a novel evidence distance measure incorporating experts’ hesitation degrees is developed, leading to a reliability-based information fusion model. The evidential linguistic CoCoSo method is then applied to support systematic technology selection for early warning systems. The effectiveness of the proposed approach is demonstrated through a case study involving the selection of technical alternatives for a multi-hazard early warning system in Sichuan Province, China. The results show that the method provides robust and effective decision support for early warning system technology selection, offering valuable technical guidance for emergency managers and stakeholders involved in early warning system development and implementation.</p>