Commercial microwave links (CMLs) are widely used as opportunistic rainfall sensors, but their network management telemetry often also includes built-in temperature readings intended for hardware supervision. This study evaluates whether such telemetry can provide useful information on ambient-temperature variability. We combine a controlled laboratory experiment, infrared imaging, a rooftop field comparison with a nearby reference weather station, and blocked-validation regression experiments across multiple roofs and technologies. The results show that CML temperature readings strongly track the temporal variability of nearby air temperature, but they are affected by large offsets caused by device self-heating, solar radiation, convection and radiative exchange with nearby structures, and post-reset warm-up. For the investigated hardware, monthly mean offsets are approximately 24 to 26 °C, confirming that CML telemetry cannot be interpreted as a direct air-temperature measurement. However, the offsets contain systematic components and the temporal co-variability with reference temperature remains strong. We therefore interpret CML temperatures as opportunistic thermal proxies rather than replacements for standard meteorological observations. In the larger multi-roof dataset, blocked validation shows that much of the apparent improvement over a global constant offset is explained by per-endpoint bias removal: the leave-one-month MAE decreases from 7.46 °C for a global offset to 1.97 °C for a per-endpoint offset. The best supervised temporal model further reduces the error against the nearby weather-station reference to 1.70 °C, whereas the best leave-one-roof result remains 2.19 °C, indicating unresolved site-transfer limitations. The errors quantify agreement with nearby reference stations rather than colocated rooftop air-temperature accuracy. Together, the results suggest that CML temperature telemetry has practical potential as a dense environmental thermal proxy, including future urban applications that exploit the range of installation heights in dense networks, provided that device-specific biases and local installation effects are explicitly accounted for. We discuss these limitations and the requirements for scaling the approach to larger heterogeneous networks.