Increasing atmospheric moisture content is expected to intensify precipitation extremes under climate warming. However, substantial uncertainty remains in quantifying changes in extreme precipitation due to global warming. Among the approaches used, traditional Clausius-Clapeyron (CC) scaling, based on thermodynamic arguments, often lacks rigor in defining ”extremes”. To address this, we propose a novel framework using the Metastatistical Extreme Value Distribution that allows the entire probability distribution of precipitation to change with warming, influencing extremes defined by return periods. Besides thermodynamics, we also examine the controls exerted by changes in large-scale atmospheric circulation at different temporal scales. Our results show that thermodynamics predominantly controls hourly precipitation, resulting in distributional characteristics which makes the rate of increase in extremes return-period-dependent. In contrast, daily precipitation is dominated by large-scale circulation, leading to significant uncertainty in establishing the temperature dependence that generates extremes. These findings reveal counteracting effects of atmospheric thermodynamics and large scale controls on the sensitivity of precipitation extremes to temperature, and are pivotal for crafting adaptation strategies to mitigate the risks associated with extreme precipitation under the backdrop of global warming.