In this study, a novel method for splicing LiDAR temperatures was proposed to solve the problem of low LiDAR detection heights when fog-haze conditions were encountered. Accordingly, a typical fog-haze case was selected as the research sample. High-resolution weather research and forecasting (WRF) model temperatures were specifically used to splice LiDAR temperatures. The splicing method focused on key technologies, including a fitting region selection technique, a coordinate height layer analysis method, a correction method between model data and LiDAR data, an optimal splicing region selection method, and an evaluation method for splicing results. The maximum height of splicing data was extended to approximately 20 km, including the entire troposphere and the lower-middle stratosphere. This was especially larger than the original height of the LiDAR data (2 km). According to a series of detailed quality assessments, the splicing data were very reliable, with a perfect match trend between the splicing profile and the standard profile and a maximum error of less than 1.5%. There was a better fit between LiDAR data and model data in the optimal splicing region. The advantages of both model data and LiDAR data were fully exploited in the proposed splicing method. Based on this, the data with a larger detection layer and high-quality temperature profile was reconstructed. Moreover, the proposed splicing method was also suitable for other complex weather conditions.