Natural climate disasters and extreme climate and weather events have caused great impacts and loses to human society and the ecological environment with global warming. Extreme climate and weather events usually are researched with extreme theory. Extreme theory is an important branch of probability theory and has had extensive application, mainly to study random samples or probability of extreme values and statistical inference in stochastic processes. While extreme value events (EVE) are very important, an equally important issue is the near-extreme value events(NEVE), i.e., how many events occur with their values near the extreme? In other words, the issue is whether the global maximum (or minimum) value is very far from others (is it lonely at the top?), or whether there are many other events whose values are close to the maximum value. These questions are talking about the near-extreme events(NEVE).
The estimation of generalized state density of near-extreme events is a combination of the near-extreme events theory and the density of states, which provides a method to quantitatively research NEVE. Based on the estimated approximate function of the mean-generalized density of states, parameters in the generalized state density of near-extreme events were constructed on the daily maximum temperature records for 1961- 2013 in China. The maximum probability density of occurrence, which presents the maximum crowding degree of near-extreme events is defined as CDNEE. The corresponding difference between extreme value and near-extreme value of CDNEE is defined as the most probable intensity of near-extreme events (INEE), which indicates when the difference between near-extreme events and extreme events is INEE, the probability of occurrence is maximum. CDNEE and INEE have significant physical meanings for practical application.
Results/Conclusions
The spatial distribution of near-extreme high temperature events in China is analyzed, which shows significant regional variation in characteristics. The generalized state density of near-extreme events has a good warning effect on extreme high temperature events. In summer, extreme high temperature events happen in parts of of Northwest, Southwest and Southern China, when INEE is [1,2.6] ℃. These regions should be given more attention. CDNEE and INEE can be regarded as warning information about extreme heat wave events. How can near-extreme event crowding properties be used as a warning index? What are the possible connections between changes in near-extreme events and global warming? These questions deserve further research.