Spatial maps of extreme precipitation are a critical component of flood estimation in hydrological modeling, as well as in the planning and design of important infrastructure. This is particularly relevant in countries such as Norway that have a high density of hydrological power generating facilities and are exposed to significant risk of infrastructure damage due to flooding. In this work, we estimate a spatially coherent map of the distribution of extreme hourly precipitation in Norway, in terms of return levels, by linking generalized extreme value (GEV) distributions with latent Gaussian fields in a Bayesian hierarchical model. Generalized linear models on the parameters of the GEV distribution are able to incorporate location-specific geographic and meteorological information and thereby accommodate these effects on extreme precipitation. Our model incorporates a Bayesian model averaging component that directly assesses model uncertainty in the effect of the proposed covariates. Gaussian fields on the GEV parameters capture additional unexplained spatial heterogeneity and overcome the sparse grid on which observations are collected. Our framework is able to appropriately characterize both the spatial variability of the distribution of extreme hourly precipitation in Norway, and the associated uncertainty in these estimates.

Regional climate models represent a valuable tool in climate impact analyses. Their ability to accurately estimate current and future climate conditions is increasingly important. In Norway precipitation is of special interest. Heavy precipitation, particularly over short durations, is responsible for enormous damages to infrastructure such as roads and railways, hence information on a fine spatial and temporal scale is crucial. We evaluate the ability of seven regional climate model simulations of 0.11° resolution from the CORDEX ensemble in reproducing 3‐h and 24‐h accumulated summer precipitation characteristics in Norway. The two‐step evaluation includes comparison of modelled precipitation to gridded observation‐based datasets and to station measurements in terms of the following indices: summer maxima, summer wet event frequency, and total summer precipitation. We find a general overestimation by the models for all indices, with only few exceptions. Country‐wide spatial averages show, however, that simulated summer maxima are mainly within the uncertainty interval of the gridded reference dataset. This might also be true for summer wet event frequency, although the comparison to station measurements indicates that the positive bias is significant. We find the largest deviation between models in the evaluation of summer totals. The spatial distribution of the different precipitation indices is fairly well simulated, although the precipitation gradients evident in observation‐based datasets appear weak in the models. We believe the high spatial resolution improves the simulations of extreme precipitation in Norway, especially in areas of orographic enhancement.

Observed trends in annual maximum snow deptin (SD) in Norway are analyzed and examined in tiie context of changes in winter climate from 1961 untii today. Trends are evaluated for the 50-year period (1961-2010) and for three 30-year periods (1961-1990,1971-2000,1981-2010). The analyzed dataset is the most extensive and geographically representative for the country so far, and the analysis gives an up-to-date picture of the recent development in snow accumulation, in regions characterized by colder winter climate long-term trends are found to be positive in general, while short-term trends shift from strongly positive in the first period to predominantly negative in the last period. Variation in SD is here mainly linked to variation in precipitation. In regions of warmer winter climate variation in SD is dominated by temperature, and iong-term trends are mainly negative.Short-term trends start out weak overall in the first period but become strongly negative most places in the last period. It is likely that, although more and more regions in Norway will experience declining maximum annual SD in a projected wetter and warmer future ciimate, some inland and higher mountain regions may still accumulate more snow in the coming decades.