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Detection and attribution of precipitation variability are fundamentally challenging, especially in the presence of complex nonlinear relationships between precipitation variability and large‐scale teleconnections. The aim of this study is twofold. First, we identify abrupt changes and the trend and periodicity characteristics of long‐term (1950–2019) annual precipitation series over the Norway mainland by using the Multiple Comparison Procedures, the Mann‐Kendall test, and the Wavelet Analysis. Second, we interpret the variability characteristics found over five regions of Norway by exploring their relationships with teleconnections through the Maximal Information Coefficient. The results indicate that significant abrupt changes appeared in the mean (variance) value of annual precipitation series at 117 (49) out of 159 rainfall stations of five regions in Norway at a significance level of 0.05. The occurrences of change points varied from 1979 to 1984 in five regions of Norway. The mean and variance of the annual precipitation series increased by 32% and 16% at most, respectively, compared with those values before the change points. The first periodicity (spanning four to five decades) was the dominant periodic component and could be used to best characterize the Norwegian precipitation variability. Because the subtropical Azores High (subpolar Icelandic Low) moves toward (away from) Europe, the relationship between the annual precipitation series and the Scandinavian pattern (Atlantic Multidecadal Oscillation) series tended to be of an upward (downward) hook structure. The association of precipitation variability and teleconnections found in this study can pave the way for new possibilities with regard to detection and attribution of precipitation variability. Key Points: Significant abrupt changes, increases and periodicities (40–50 years) constitute the Norwegian annual precipitation variabilityThe maximal information coefficient adequately associates the Norwegian precipitation variability with teleconnectionsThe maximal information coefficient captures hook structures of relationships between precipitation and teleconnections [ABSTRACT FROM AUTHOR]

• Two-component mixture distributions (TCMD) are used to model annual maximum floods. • Usage of flood timescale (FT) to classify distinct flood generating mechanisms. • Snowmelt-induced floods and rainfall-induced floods are identified in Norway. • The FT -based TCMD reduces uncertainty of design flood by 40% than traditional TCMD. The conventional flood frequency analysis typically assumes the annual maximum flood series (AMFS) result from a homogeneous flood population. However, actually AMFS are frequently generated by distinct flood generation mechanisms (FGMs), which are controlled by the interaction between different meteorological triggers (e.g., thunderstorms, typhoon, snowmelt) and properties of underlying surface (e.g., antecedent soil moisture and land-cover types). To consider the possibility of two FGMs in flood frequency analysis, researchers often use the two-component mixture distributions (TCMD) without explicitly linking each component distribution to a particular FGM. To improve the mixture distribution modeling in seasonally snow covered regions, an index called flood timescale (FT), defined as the ratio of the flood volume to peak value and chosen to reflect the relevant FGM, is employed to classify each flood into one of two types, i.e., the snowmelt-induced long-duration floods and the rainfall-induced short-duration floods, thus identifying the weighting coefficient of each component distribution beforehand. In applying the FT -based TCMD to model the AMFS of 34 watersheds in Norway, ten types of mixture distributions are considered. The design floods and associated confidence intervals are calculated using parametric bootstrap method. The results indicate that the FT -based TCMD model reduces the uncertainty in the estimation of design floods for high return periods by up to 40% with respect to the traditional TCMD. The improved predictive ability of the FT -based TCMD model is attributed to its explicit recognition of distinct generation mechanisms of floods, thereby being able to identify the weighting coefficient and FGM of each component distribution without optimization. [ABSTRACT FROM AUTHOR]