Detailed investigations of time series features across climates, continents and variable types can progress our understanding and modelling ability of the Earth’s hydroclimate and its dynamics. They can also improve our comprehension of the climate classification systems appearing in their core. Still, such investigations for seasonal hydroclimatic temporal dependence, variability and change are currently missing from the literature. Herein, we propose and apply at the global scale a methodological framework for filling this specific gap. We analyse over 13,000 earth-observed quarterly temperature, precipitation and river flow time series. We adopt the Köppen–Geiger climate classification system and define continental-scale geographical regions for conducting upon them seasonal hydroclimatic feature summaries. The analyses rely on three sample autocorrelation features, a temporal variation feature, a spectral entropy feature, a Hurst feature, a trend strength feature and a seasonality strength feature. We find notable differences to characterize the magnitudes of these features across the various Köppen–Geiger climate classes, as well as between continental-scale geographical regions. We, therefore, deem that the consideration of the comparative summaries could be beneficial in water resources engineering contexts. Lastly, we apply explainable machine learning to compare the investigated features with respect to how informative they are in distinguishing either the main Köppen–Geiger climates or the continental-scale regions. In this regard, the sample autocorrelation, temporal variation and seasonality strength features are found to be more informative than the spectral entropy, Hurst and trend strength features at the seasonal time scale.

对气候、大陆和变量类型的时间序列特征的详细研究可以提高我们对地球水文气候及其动力学的理解和建模能力。它们还可以提高我们对其核心中出现的气候分类系统的理解。尽管如此，目前文献中还缺少对季节性水文气候时间依赖性、变异性和变化的研究。在此，我们提出并在全球范围内应用一个方法框架来填补这一特定空白。我们分析了 13,000 多个地球观测的季度温度、降水和河流流量时间序列。我们采用柯本-盖革气候分类系统，定义大陆尺度的地理区域，以便对其进行季节性水文气候特征总结。该分析依赖于三个样本自相关特征：时间变化特征、谱熵特征、赫斯特特征、趋势强度特征和季节性强度特征。我们发现在不同的柯本-盖革气候类别以及大陆规模的地理区域之间描述这些特征的程度存在显着差异。因此，我们认为，考虑比较总结对于水资源工程背景可能是有益的。最后，我们应用可解释的机器学习来比较所研究的特征，看看它们在区分主要柯本-盖革气候或大陆范围区域方面的信息量。在这方面，样本自相关，