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A fundamental issue when evaluating the simulation of precipitation is the difficulty of quantifying specific sources of errors and recognizing compensation of errors. We assess how well a large ensemble of high‐resolution simulations represents the precipitation associated with strong cyclones. We propose a framework to breakdown precipitation errors according to different dynamical (vertical velocity) and thermodynamical (vertically integrated water vapor) regimes and the frequency and intensity of precipitation. This approach approximates the error in the total precipitation of each regime as the sum of three terms describing errors in the large‐scale environmental conditions, the frequency of precipitation and its intensity. We show that simulations produce precipitation too often, that its intensity is too weak, that errors are larger for weak than for strong dynamical forcing and that biases in the vertically integrated water vapor can be large. Using the error breakdown presented above, we define four new error metrics differing on the degree to which they include the compensation of errors. We show that convection‐permitting simulations consistently improve the simulation of precipitation compared to coarser‐resolution simulations using parameterized convection, and that these improvements are revealed by our new approach but not by traditional metrics which can be affected by compensating errors. These results suggest that convection‐permitting models are more likely to produce better results for the right reasons. We conclude that the novel decomposition and error metrics presented in this study give a useful framework that provides physical insights about the sources of errors and a reliable quantification of errors.

Increased temperature will result in longer, more frequent, and more intense heat waves. Changes in temperature variability have been deemed necessary to account for future heat wave characteristics. However, this has been quantified only in Europe and North America, while the rest of the globe remains unexplored. Using late century global climate projections, we show that annual mean temperature increases is the key factor defining heat wave changes in most regions. We find that commonly studied areas are an exception rather than the standard and the mean climate change signal generally outweighs any influence from variability changes. More importantly, differences in warming across seasons are responsible for most of the heat wave changes and their consideration relegates the contribution of variability to a marginal role. This reveals that accurately capturing mean seasonal changes is crucial to estimate future heat waves and reframes our interpretation of future temperature extremes.

This study evaluates the added value in the representation of surface climate variables from an ensemble of regional climate model (RCM) simulations by comparing the relative skill of the RCM simulations and their driving data over a wide range of RCM experimental setups and climate statistics. The methodology is specifically designed to compare results across different variables and metrics, and it incorporates a rigorous approach to separate the added value occurring at different spatial scales. Results show that the RCMs’ added value strongly depends on the type of driving data, the climate variable, and the region of interest but depends rather weakly on the choice of the statistical measure, the season, and the RCM physical configuration. Decomposing climate statistics according to different spatial scales shows that improvements are coming from the small scales when considering the representation of spatial patterns, but from the large-scale contribution in the case of absolute values. Our results also show that a large part of the added value can be attained using some simple postprocessing methods.

While it is established that low-frequency climate variability modes have a dominant role on Australia's climate, limited work to date has focused on relationships between climate variability and Australian heatwaves. Moreover, heatwaves are a distinctive type of extreme weather that can be classified by multiple characteristics, such as intensity, frequency, duration, and timing. This study identifies the relationships between known modes of climate variability that influence Australian climate, and discrete seasonal characteristics of the intensity, frequency, duration, and timing of heatwaves. The large-scale seasonal modes of the El Nino/Southern Oscillation (ENSO), the Indian Ocean Dipole (IOD), and the Southern Annular Mode (SAM) are investigated for extended Austral summers commencing between the years 1911 and 2012. While ENSO is found to have the strongest relationship with Australian heatwave characteristics, this study finds that ENSO's influence differs between heatwave frequency, duration, intensity, and timing. Regions dominated by ENSO experience more, longer lasting and hotter heatwaves combined with an earlier commencement of the heatwave season during El Nino phases. The exception to this is southeast Australia, where SAM is generally more dominant. In contrast, the IOD provides little indication of seasonal heatwave characteristics due to its relative inactivity during the Austral summer months. Lastly, we show that antecedent soil moisture has varying strengths of relationships with Australian heatwave characteristics, exhibiting relationships with heatwave intensity and timing over some regions where none are detected between large-scale modes. However, while significant relationships between dry antecedent soil moisture and extreme heatwaves do exist over Australia, these appear to be slightly weaker than similar relationships over Europe reported in other studies.

Identifying the relationship between large-scale climate signals and seasonal streamflow may provide a valuable tool for long-range seasonal forecasting in regions under water stress, such as the Iberian Peninsula (IP). The skill of the main teleconnection indices as predictors of seasonal streamflow in the IP was evaluated. The streamflow database used was composed of 382 stations, covering the period 1975–2008. Predictions were made using a leave-one-out cross-validation approach based on multiple linear regression, combining Variance Inflation Factor and Stepwise Backward selection to avoid multicollinearity and select the best subset of predictors. Predictions were made for four forecasting scenarios, from one to four seasons in advance. The correlation coefficient (RHO), Root Mean Square Error Skill Score (RMSESS), and the Gerrity Skill Score (GSS) were used to evaluate the forecasting skill. For autumn streamflow, good forecasting skill (RHO>0.5, RMSESS>20%, GSS>0.4) was found for a third of the stations located in the Mediterranean Andalusian Basin, the North Atlantic Oscillation of the previous winter being the main predictor. Also, fair forecasting skill (RHO>0.44, RMSESS>10%, GSS>0.2) was found in stations in the northwestern IP (16 of these located in the Douro and Tagus Basins) with two seasons in advance. For winter streamflow, fair forecasting skill was found for one season in advance in 168 stations, with the Snow Advance Index as the main predictor. Finally, forecasting was poorer for spring streamflow than for autumn and winter, since only 16 stations showed fair forecasting skill in with one season in advance, particularly in north-western of IP.

[1] A set of eight simulations at high spatial resolution (10 km) were performed with WRF to generate future projections of mean and extreme precipitation over Spain. The model was driven with two GCMs (ECHAM5 and CCSM3.0) under the conditions of three SRES emissions scenarios (B1, A1B and A2) which amounts to six future (2070–2099) climate simulations. Two present (1970–1999) climate simulations forced by the same GCMs were previously completed and were used as a baseline to allow for comparison and quantify the changes. The annual and seasonal precipitation means were examined to elucidate how global warming will manifest in the future precipitation climatic means. The distribution of precipitation in different-intensity events and the percentiles were calculated to further describe the nature of the changes. Additionally, a number of extreme indices were explored to determine changes in the low frequency events and the persistency of specific conditions. The results indicate that Spain might be exposed to a substantial decrease in annual precipitation that range between −18% and −42% depending on the simulation, with a particularly severe reduction during the summer, between −32% and −72%. These changes are generally caused by less light-to-moderate events, which leads to a displacement of the probability density function toward higher values. Namely, future climate might be characterized by less precipitation but more concentrated in extreme events, although their occurrence in absolute terms might not vary. The projections also suggest that, by the end of the century, the wet periods might be shorter and the dry spells significantly longer.