Climate models simulate extreme precipitation under nonstationarity due to continuous climate change. However, systematic errors in local‐scale climate projections are often corrected using stationary or quasi‐stationary methods without explicit and continuous nonstationarity treatment, like quantile mapping (QM), detrended QM, and quantile delta mapping. To bridge this gap, we introduce nonstationary QM (NS‐QM) and its simplified version for consistent nonstationarity patterns (CNS‐QM). Besides, correction approaches for extremes often rely on limited extreme‐event records. To leverage ordinary‐event information while focusing on extremes, we propose integrating the simplified Metastatistical extreme value (SMEV) distribution into NS‐QM and CNS‐QM (NS‐QM‐SMEV and CNS‐QM‐SMEV). We demonstrate the superiority of NS‐ and CNS‐QM‐SMEV over existing methods through a simulation study and show several real‐world applications using high‐resolution‐regional and coarse‐resolution‐global climate models. NS‐QM and CNS‐QM reflect nonstationarity more realistically but may encounter challenges due to data limitations like estimation errors and uncertainty, particularly for the most extreme events. These issues, shared by existing approaches, are effectively mitigated using the SMEV distribution. NS‐ and CNS‐QM‐SMEV offer lower estimation error, approximate unbiasedness, reduced uncertainty, and improved representation of the entire distribution, especially for samples of ∼70 years, and greater superiority with larger samples. We show existing methods may perform competitively for short samples but exhibit substantial biases in quantile‐quantile matching due to bypassing nonstationarity modeling. NS‐ and CNS‐QM‐SMEV avoid these biases, adhering better to their theoretical functioning. Thus, NS‐ and CNS‐QM‐SMEV enhance the correction of extremes under nonstationarity. Yet, properly identifying nonstationarity patterns is crucial for reliable implementations.

The Elbow River is an eastern slope river with headwaters in the Rocky Mountains in Alberta whose major end-use is a critical source of municipal water for Calgary. Overwinter precipitation in its watershed falls primarily as snow and accumulates as snowpack until spring melt. Precipitation falls mainly as rain from May until October. The river is unregulated above Calgary’s water supply reservoir, and its relatively undeveloped watershed makes it ideal for examining potential climate change impacts on river hydrology. Available historical hydrometeorological data (1967 to 2015) from the basin were assessed to study its hydrological behaviour under a changing climate. The analysis showed significant upward trends in both flow and precipitation variables, especially from 1979 to 2015. Significant increases in both annual flow volume and annual maximum daily flow (AM-flow), and later seasonal occurrence of AM-flow, were not observed in other eastern slope rivers. Although these changes could attenuate predicted water supply shortages, they could also potentially increase flood magnitudes. The analysis also revealed that three sub-watersheds, which are approximately equal in geographic area, contributed differing flow volumes during the high-flow season (May to October). The upper watershed contributed most (∼68%), followed by the middle (∼26%) and lower (∼6%) watersheds, on average. Extreme high-flow events (ie >90^th percentile AM-flow) were strongly related to high rainfall events, but not significantly related to snowpack loss (or melt). Moderate AM-flows were positively related to both the cumulative snowpack loss before the high-flow season and the cumulative antecedent precipitation prior to the AM-flow, suggesting that the antecedent soil moisture conditions could play a role. Predictions of climate change impacts on this eastern slope river’s hydrology should thus consider the effects of meteorological variables and the moisture conditions of the watershed.

The presence of the nonstationarity in flow datasets has challenged the flood hazard assessment. Nonstationary tools and evaluation metrics have been proposed to deal with the nonstationarity and guide the infrastructure design and mitigation measures. To date, the examination of how the flood hazards are affected by the nonstationarity is still very limited. This paper thus examined the association between the flood hazards and the nonstationary patterns and degrees of the underlying datasets. The Particle Filter, which allows for assessing the uncertainty of the point estimates, was adopted to conduct the nonstationary flood frequency analysis (NS-FFA) for subsequently estimating the flood hazards in three real study cases. The results suggested that the optimal and top NS-FFA models selected according to the fitting efficiency in general align with the pattern of nonstationarity, although they might not always be superior in terms of uncertainty. Moreover, the results demonstrated the association and the sensitivity of the flood hazards to the perceived patterns and degrees of nonstationarity. In particular, the variations of the flood hazards intensified with the increase in the degree of nonstationarity, which should be assessed in a more elaborate manner, i.e., considering multiple statistical moments. These advocate the potential of using the nonstationarity characteristics as a proxy for evaluating the evolutions of the flood hazards.