This paper explores different goodness-of-fit (GOF) criteria’s used in the various fields of science to compare candidate probability density functions (pdfs) to annual maximum temperature records and discusses their usefulness and drawbacks. The L-moment ratio diagram method is also proposed as alternative approach for the GOF of the pdfs. The advantage this method allows for an easy comparison of the fit of many pdfs for several stations on a single diagram. To gain knowledge about higher order moments (i.e. shape, skewness and kurtosis) of the station data set, plotting the position of a given temperature data set in L-moment ratio diagram space is prompt and effective and can provide a useful addition to the GOF criterion. Both the L-moment ratio diagrams and many GOF criteria are used on real data to assess the fit of the pdfs for temperature data in the state of New South Wales, Australia. The analysis of the L-moment ratio diagrams reveals that the generalized extreme value and normal distributions generally fit best the annual maximum temperature series. The other two- and three-parameter distributions also showed viable fits in some instances. Results obtained from L-moment diagrams, temperature frequency histograms, cumulative density plots and the simulation study are compared with those obtained from GOF statistics, and a good agreement is generally observed between all these approaches. In conclusion the L-moment ratio diagram can represent a simple, effective and efficient approach to be used as a complementary method along with the traditional GOF criteria.

本文探讨了在各个科学领域中使用的不同的拟合优度（GOF）标准，以将候选概率密度函数（pdfs）与年度最高温度记录进行比较，并讨论了其有用性和缺点。还建议使用L矩比图方法作为pdfs GOF的替代方法。这种方法的优点是可以轻松比较单个图表上多个站点的许多pdf的拟合度。为了获得有关站点数据集的高阶矩（即形状，偏斜和峰度）的知识，在L矩比图空间中绘制给定温度数据集的位置是迅速而有效的，并且可以为GOF提供有用的补充标准。L矩比图和许多GOF准则都用于实际数据，以评估澳大利亚新南威尔士州温度数据的pdf拟合。对L矩比图的分析表明，广义的极值和正态分布通常最适合年度最高温度序列。在某些情况下，其他两参数和三参数分布也显示出可行的拟合。从L矩图，温度频率直方图，累积密度图和模拟研究获得的结果与从GOF统计获得的结果进行了比较，并且在所有这些方法之间通常都观察到了很好的一致性。总之，L矩比图可以表示一个简单的