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Action towards climate change mitigation depends on perceptions of severity. With the digital revolution, climate communication is no longer restricted only to climate experts but it has extended to general public as well. This shift raises a question whether experts and general public equally persuade people about the grave need for climate mitigation. In a dynamic social media setting, fragmented attention and contesting content suits the peripheral route of persuasion, where easily readable, and emotionally appealing prompts often captures attention compared to complicated reasoning based on science. Restricted cognitive elaboration among users with low motivation, leads to heterogenous engagement patterns ranging from critical assessment to responses driven by heuristics, causing asymmetries in climate communication. Hence this study applied Elaboration Likelihood Model (ELM), a type of dual-process theory, on a dataset of climate change tweets to analyse how readability of tweets and complexity of messages (central cues) interact with source credibility (peripheral cues) in shaping users’ engagement on X. Although climate change communication strategies are widely analysed empirical research integrating framing, source expertise and information processing routes in a dynamic social media setting remains restricted. Addressing this gap, the present study aims to examine variations in the readability, engagement, and cognitive framing of climate change discourses on X between experts and general public. Comparing linguistic comprehensibility, user engagement metrics, and shifts in expert communication during important climatic milestones, this study aims to comprehend how message characteristics and source expertise shape public interaction with climate content on social media.This study compares readability scores and engagement metrics (likes, replies, retweets) on an anthropogenic climate change tweets dataset (January 2022 to May 2023) containing 333,635 original tweets. The tweets were clustered into four thematic areas: scientific, anthropogenic, policy, and conspiracy narratives. We found that expert’s tweets were significantly more complicated with lower reading ease score and higher complexity score. Specifically, such observations were reported in anthropogenic, scientific, and conspiracy clusters for experts. No significant variations emerged in the policy cluster, suggesting comparable readability among experts and general public. Cluster-level analyses indicated that expert-authored tweets consistently garner greater engagement compared to tweets by general public. Across all clusters, retweets were found to be higher in the experts’ tweets. Variations in reply are significant only in scientific and policy clusters. Engagement analysis showed experts consistently outperformed the general public, with significantly more likes and retweets, particularly for scientific and policy content. Expertise strongly boosted engagement (peripheral route), while higher reading ease further amplified this effect, especially for experts. Conversely, higher complexity modestly increased engagement overall but reduced the marginal benefit of expertise for likes. Temporal analysis around major climate milestones revealed spikes in expert activity and thematic shifts, with discourse patterns influenced by cognitive biases, including authority bias, confirmation bias, and group polarisation. The results demonstrate that climate communication on social media is shaped by the interaction of source expertise, message accessibility, and cognitive biases, with implications for science communication and public engagement.