  The frequency and magnitude of weather extreme events have increased
significantly during the past few years in response to anthropogenic climate
change. However, global statistical characteristics and underlying physical
mechanisms are still not fully understood. Here, we adopt a statistical physics
and probability theory based method to investigate the nature of extreme
weather events, particularly the statistics of the day-to-day air temperature
differences. These statistical measurements reveal that the distributions of
the magnitudes of the extreme events satisfy a universal \textit{Gumbel}
distribution, while the waiting time of those extreme events is governed by a
universal \textit{Gamma} function. Further finite-size effects analysis
indicates robust scaling behaviours. We additionally unveil that the cumulative
distribution of logarithmic waiting times between the record events follows an
\textit{Exponential} distribution and that the evolution of this climate system
is directional where the underlying dynamics are related to a decelerating
release of tension. The universal scaling laws are remarkably stable and
unaffected by global warming. Counterintuitively, unlike as expected for record
dynamics, we find that the number of quakes of the extreme temperature
variability does not decay as one over time but with deviations relevant to
large-scale climate extreme events. Our theoretical framework provides a fresh
perspective on the linkage of universality, scaling, and climate systems. The
findings throw light on the nature of the weather variabilities and could guide
us to better forecast extreme events.
