Real-time urban climate monitoring provides useful information that can be utilized to help monitor and adapt to extreme events, including urban heatwaves. Typical approaches to the monitoring of cli-mate data include the acquisition of weather station monitoring and also remote sensing via satellite sensors. However, climate monitoring stations are very often distributed spatially in a sparse manner, and consequently, this has a significant impact on the ability to reveal exposure risks due to extreme climates at an intra-urban scale (e.g., street level). Additionally, such traditional remote sensing data sources are typically not received and analyzed in real-time which is often required for adaptive urban management of climate extremes, such as sudden heatwaves. Fortunately, recent social media, such as Twitter, furnishes real-time and high-resolution spatial information that might be useful for climate condition estimation. The objective of this study is utilizing geo-tagged tweets (participatory sensing data) for urban tem-perature analysis. We first detect tweets relating hotness (hot-tweets). Then, we study relationships between monitored temperatures and hot-tweets via a statistical model framework based on copula modelling methods. We demonstrate that there are strong relationships between "hot-tweets" and tem-peratures recorded at an intra-urban scale, that we reveal in our analysis of Tokyo city and its suburbs. Subsequently, we then investigate the application of "hot-tweets" informing spatio-temporal Gaussian process interpolation of temperatures as an application example of "hot-tweets". We utilize a combina-tion of spatially sparse weather monitoring sensor data, infrequently available MODIS remote sensing data and spatially and temporally dense lower quality geo-tagged twitter data. Here, a spatial best linear unbiased estimation (S-BLUE) technique is applied. The result suggests that tweets provide some useful auxiliary information for urban climate assessment. Lastly, effectiveness of tweets toward a real-time urban risk management is discussed based on the analysis of the results.