Approximately 10 years ago, convection‐permitting regional climate models (CPRCMs) emerged as a promising computationally affordable tool to produce fine resolution (1–4 km) decadal‐long climate simulations with explicitly resolved deep convection. This explicit representation is expected to reduce climate projection uncertainty related to deep convection parameterizations found in most climate models. A recent surge in CPRCM decadal simulations over larger domains, sometimes covering continents, has led to important insights into CPRCM advantages and limitations. Furthermore, new observational gridded datasets with fine spatial and temporal (~1 km; ~1 h) resolutions have leveraged additional knowledge through evaluations of the added value of CPRCMs. With an improved coordination in the frame of ongoing international initiatives, the production of ensembles of CPRCM simulations is expected to provide more robust climate projections and a better identification of their associated uncertainties. This review paper presents an overview of the methodology to produce CPRCM simulations and the latest research on the related added value in current and future climates. Impact studies that are already taking advantage of these new CPRCM simulations are highlighted. This review paper ends by proposing next steps that could be accomplished to continue exploiting the full potential of CPRCMs.This article is categorized under: Climate Models and Modeling > Earth System Models

Weather radar has become an invaluable tool for monitoring rainfall and studying its link to hydrological response. However, when it comes to accurately measuring small-scale rainfall extremes responsible for urban flooding, many challenges remain. The most important of them is that radar tends to underestimate rainfall compared to gauges. The hope is that by measuring at higher resolutions and making use of dual-polarization radar, these mismatches can be reduced. Each country has developed its own strategy for addressing this issue. However, since there is no common benchmark, improvements are hard to quantify objectively. This study sheds new light on current performances by conducting a multinational assessment of radar's ability to capture heavy rain events at scales of 5 min up to 2 h. The work is performed within the context of the joint experiment framework of project MUFFIN (Multiscale Urban Flood Forecasting), which aims at better understanding the link between rainfall and urban pluvial flooding across scales. In total, six different radar products in Denmark, the Netherlands, Finland and Sweden were considered. The top 50 events in a 10-year database of radar data were used to quantify the overall agreement between radar and gauges as well as the bias affecting the peaks. Results show that the overall agreement in heavy rain is fair (correlation coefficient 0.7–0.9), with apparent multiplicative biases on the order of 1.2–1.8 (17 %–44 % underestimation). However, after taking into account the different sampling volumes of radar and gauges, actual biases could be as low as 10 %. Differences in sampling volumes between radar and gauges play an important role in explaining the bias but are hard to quantify precisely due to the many post-processing steps applied to radar. Despite being adjusted for bias by gauges, five out of six radar products still exhibited a clear conditional bias, with intensities of about 1 %–2 % per mmh⁻¹. As a result, peak rainfall intensities were severely underestimated (factor 1.8–3.0 or 44 %–67 %). The most likely reason for this is the use of a fixed Z–R relationship when estimating rainfall rates (R) from reflectivity (Z), which fails to account for natural variations in raindrop size distribution with intensity. Based on our findings, the easiest way to mitigate the bias in times of heavy rain is to perform frequent (e.g., hourly) bias adjustments with the help of rain gauges, as demonstrated by the Dutch C-band product. An even more promising strategy that does not require any gauge adjustments is to estimate rainfall rates using a combination of reflectivity (Z) and differential phase shift (Kdp), as done in the Finnish OSAPOL product. Both approaches lead to approximately similar performances, with an average bias (at 10 min resolution) of about 30 % and a peak intensity bias of about 45 %.

Regional climate model simulations have routinely been applied to assess changes in precipitation extremes at daily time steps. However, shorter sub-daily extremes have not received as much attention. This is likely because of the limited availability of high temporal resolution data, both for observations and for model outputs. Here, summertime depth duration frequencies of a subset of the EURO-CORDEX 0.11^∘ ensemble are evaluated with observations for several European countries for durations of 1 to 12 h. Most of the model simulations strongly underestimate 10-year depths for durations up to a few hours but perform better at longer durations. The spatial patterns over Germany are reproduced at least partly at a 12 h duration, but all models fail at shorter durations. Projected changes are assessed by relating relative depth changes to mean temperature changes. A strong relationship with temperature is found across different subregions of Europe, emission scenarios and future time periods. However, the scaling varies considerably between different combinations of global and regional climate models, with a spread in scaling of around 1–10 % K⁻¹ at a 12 h duration and generally higher values at shorter durations.

Historical in situ sub-daily rainfall observations are essential for the understanding of short-duration rainfall extremes but records are typically not readily accessible and data are often subject to errors and inhomogeneities. Furthermore, these events are poorly quantified in projections of future climate change making adaptation to the risk of flash flooding problematic. Consequently, knowledge of the processes contributing to intense, short-duration rainfall is less complete compared with those on daily timescales. The INTENSE project is addressing this global challenge by undertaking a data collection initiative that is coupled with advances in high-resolution climate modelling to better understand key processes and likely future change. The project has so far acquired data from over 23 000 rain gauges for its global sub-daily rainfall dataset (GSDR) and has provided evidence of an intensification of hourly extremes over the US. Studies of these observations, combined with model simulations, will continue to advance our understanding of the role of local-scale thermodynamics and large-scale atmospheric circulation in the generation of these events and how these might change in the future.

This paper complements a series of now four publications that document the release of the Earth System Model Evaluation Tool (ESMValTool) v2.0. It describes new diagnostics on the hydrological cycle, extreme events, impact assessment, regional evaluations, and ensemble member selection. The diagnostics are developed by a large community of scientists aiming to facilitate the evaluation and comparison of Earth system models (ESMs) which are participating in the Coupled Model Intercomparison Project (CMIP). The second release of this tool aims to support the evaluation of ESMs participating in CMIP Phase 6 (CMIP6). Furthermore, datasets from other models and observations can be analysed. The diagnostics for the hydrological cycle include several precipitation and drought indices, as well as hydroclimatic intensity and indices from the Expert Team on Climate Change Detection and Indices (ETCCDI). The latter are also used for identification of extreme events, for impact assessment, and to project and characterize the risks and impacts of climate change for natural and socio-economic systems. Further impact assessment diagnostics are included to compute daily temperature ranges and capacity factors for wind and solar energy generation. Regional scales can be analysed with new diagnostics implemented for selected regions and stochastic downscaling. ESMValTool v2.0 also includes diagnostics to analyse large multi-model ensembles including grouping and selecting ensemble members by user-specified criteria. Here, we present examples for their capabilities based on the well-established CMIP Phase 5 (CMIP5) dataset.