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In the framework of the KlimAdat national project, the Hungarian Meteorological Service (OMSZ) is aiming to perform 10 km horizontal resolution simulations with the 2015 version of the REMO regional climate model over Central and Eastern Europe. The long-term simulations were preceded by a 10-year long sensitivity study on domain size, which is summarised in this paper. We selected three different domains embedded in each other, which contain the whole area of the Danube and Tisza river catchments. Lateral boundary conditions were obtained from the 50 km resolution REMO driven by the MPI-ESM-LR global climate model. Simulations were performed for the period of 1970–1980 including 1-year spin-up. Monthly and seasonal means of daily 2 m temperature, precipitation sum and several precipitation indices were evaluated. Reference datasets were E-OBS 19.0 and CarpatClim-HU. We can conclude, that the selection of domain size has a larger impact on the simulation of precipitation, and in the case of the seasonal mean of the precipitation indices, the differences amongst the results obtained on each model domain exceed 10 %. In general, the smallest biases occurred on the largest domain, therefore further long-term simulations are being produced on this domain.

A collection of 10 high-impact extreme precipitation events occurring in Southeastern South America during the warm season has been analyzed using statistical (ESD) and dynamical downscaling approaches. Regional Climate Models from the CORDEX database for the South American domain at two horizontal resolutions, 50 km and 25 km, short-term simulations at 20 km and at 4 km convective-permitting resolution and statistical downscaling techniques based on the analogue method and the generalized linear model approach were evaluated. The analysis includes observational datasets based on gridded data, station data and satellite products that allow assessing the observational uncertainty that characterizes extreme events in the region. It is found that the ability of the modelling strategies in capturing the main features of the extreme rainfall varies across the events. The higher the horizontal resolution of the models, the more intense and localized the core of the rainfall event, being the location of the exit region of the low-level jet and the low-level moisture flux convergence during the initial stages of the events the most relevant features that determine models’ ability of capturing the location and intensity of the core of the heavy rainfall. ESD models based on the generalized linear approach overestimate the spatial extension of the events and underestimate the intensity of the local maxima. Weather-like convective-permitting simulations depict an overall good performance in reproducing both the rainfall patterns and the triggering mechanisms of the extreme events as expected, given that these simulations are strongly controlled by the initial conditions.

The East Asia Regional Reanalysis (EARR) system is developed based on the advanced hybrid gain data assimilation method (AdvHG) using Weather Research and Forecasting (WRF) model and conventional observations. Based on EARR, the high-resolution regional reanalysis and reforecast fields are produced with 12 km horizontal resolution over East Asia for 2010–2019. The newly proposed AdvHG is based on the hybrid gain approach, weighting two different analysis for an optimal analysis. The AdvHG is different from the hybrid gain in that 1) E3DVAR is used instead of EnKF, 2) 6 h forecast of ERA5 is used to be more consistent with WRF, and 3) the pre-existing, state-of-the-art reanalysis is used. Thus, the AdvHG can be regarded as an efficient approach to generate regional reanalysis dataset due to cost savings as well as the use of the state-of-the-art reanalysis. The upper air variables of EARR are verified with those of ERA5 for January and July 2017 and the two-year period of 2017–2018. For upper air variables, ERA5 outperforms EARR over two years, whereas EARR outperforms (shows comparable performance to) ERA-I and E3DVAR for January in 2017 (July in 2017). EARR better represents precipitation than ERA5 for January and July in 2017. Therefore, though the uncertainties of upper air variables of EARR need to be considered when analyzing them, the precipitation of EARR is more accurate than that of ERA5 for both two seasons. The EARR data presented here can be downloaded from https://doi.org/10.7910/DVN/7P8MZT for data on pressure levels and https://doi.org/10.7910/DVN/Q07VRC for precipitation.

Extreme precipitation is the leading cause of the flood, soil erosion, and drought with significant socioeconomic impacts on human resources. Therefore, projecting future precipitation changes, especially the intensity of extreme precipitation (IEP) in the future, is very important. This study’s main objective is IEP projection in Iran based on CMIP6 bias-correction (BC) multi-model ensemble (MME). The daily precipitation data of five CMIP6 BC models with 0.5 horizontal resolution was used under three shared socioeconomic pathways; SSP1-2.6, SSP3-7.0, and SSP5-8.5 scenarios. Simple Daily Intensity (SDII) and maximum consecutive 1-day precipitation (RX1day) were used to measure IEP changes. Two Kling Gupta efficiency (KGE) and percent bias (PBIAS) methods evaluate the performance of the models, and the independence weighted mean (IWM) method was applied for ensemble averaging. Among the studied CMIP6 bias-correction models, the IPSL-CM6A-LR model has more underestimation than other models with a KGE score of 0.751 has the lowest performance and the MPI-ESM1-2-HR model (0.768) showed the highest performance. In general, the study results showed the uncertainties in CMIP6 models for precipitation, according to which no single model is reliable even in the BC method. The PBIAS in the region affected by Asian summer monsoon (ASM) in Iran is about 10%, based on which the bias of the mentioned models for monsoon precipitation is high. SDII and RX1day anomalies in all climatic zones of Iran except for the RX1day index in Cfa climatic zones in other zones and scenarios during the two periods 2021-2060 and 2061-2100 are positive. Also, the trend and slope of the IEP are increasing in all zones except for BWh, BWk, and Cfa zones for SSP1-2.6 and SSP3-7.0 scenarios. Investigation of trend IEP changes showed that the maximum of these changes will occur in the BWh climate zone, and the minimum would occur in the cold and mountainous climate zone of Iran (Dsc).

The impact of soil moisture initial conditions on the mean climate over West Africa was examined using the latest version of the regional climate model of the International Centre for Theoretical Physics (RegCM4) at a 25 km horizontal resolution. The soil moisture reanalysis of the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis of the 20th century (ERA-20C) was used to initialize the control experiment, while its minimum and maximum values over the entire domain were used to establish the respective initial dry and wet soil moisture conditions (hereafter referred to as dry and wet experiments, respectively). For the respective control, wet and dry experiments, an ensemble of five runs from June to September was performed. In each experiment, we analyzed the two idealized simulations most sensitive to the dry and wet soil moisture initial conditions. The impact of soil moisture initial conditions on precipitation in West Africa is linear over the Central and West Sahel regions, where dry (wet) experiments lead to a rainfall decrease (increase). The strongest precipitation increase is found over the West Sahel for wet experiments, with a maximum change value of approximately 40 %, whereas the strongest precipitation decrease is found for dry experiments over the Central Sahel, with a peak change of approximately −4 %. The sensitivity of soil moisture initial conditions can persist for 3–4 months (90–120 d) depending on the region. However, the influence on precipitation is no longer than 1 month (between 15 and 30 d). The strongest temperature decrease is located over the Central and West Sahel, with a maximum change of approximately −1.5 ∘C in wet experiments, whereas the strongest temperature increase is found over the Guinea coast and Central Sahel for the dry experiments, with a maximum change of around 0.6 ∘C. A significant impact of soil moisture initial conditions on the surface energy fluxes is noted: in the wet (dry) experiments, a cooling (warming) of the surface temperature is associated with a decrease (increase) in sensible heat flux, an increase (decrease) in latent heat flux and a decrease (increase) in the boundary layer depth. Part 2 of this study (Koné et al., 2022) investigates the influence of soil moisture initial conditions on climate extremes.

To analyze the effects of horizontal resolution on hourly precipitation, four Atmospheric Model Intercomparison Project simulations are carried out using the Chinese Academy of Sciences Earth System Model (CAS-ESM) and the Community Earth System Model (CESM) during 1998–2016. They include CAS-ESM at resolutions of 1.4° latitude × 1.4° longitude (CAS-ESM L) and 0.5° × 0.5° (CAS-ESM H), and CESM at resolutions of 1.9° latitude × 2.5° longitude (CESM L) and 0.47° × 0.63° (CESM H), respectively. We focus on the simulated hourly precipitation frequency and assess the frequency with respect to high-resolution satellite observations and reanalysis. The high-resolution experiments show some improvements of measurable precipitation (>0.02 mm h−1) frequency. Noticeable improvement of heavy rainfall (>2 mm h−1) frequency is demonstrated at the high resolutions. The zonal mean, seasonal mean, and area-weighted average frequency support the above results. The high-resolution experiments outperform the low-resolution experiments in reproducing hourly precipitation intensity and amount. The added value is apparent in heavy precipitation intensity from CAS-ESM H and CESM H. Over the monsoon regions and tropical convergence zones, the patterns of probability density functions for precipitation from high-resolution experiments are closer to the observations and reanalysis than those from the low-resolution simulations. The improvement of measurable precipitation frequency is mainly caused by the reductions of the convective rainfall occurrence at high resolutions. The increasing large-scale precipitation and reasonable integrated water vapor flux contribute to the improvements in measurable rainfall intensity and heavy precipitation characteristics. The results of this study support the concept that high-resolution global simulations could produce improved hourly precipitation capabilities, especially for heavy rainfall.

The double-ITCZ bias has puzzled the climate modeling community for more than two decades. Here we show that, over the northeastern Pacific Ocean, precipitation and sea surface temperature (SST) biases are seasonally dependent in the NCAR CESM1 and 37 CMIP5 models, with positive biases during boreal summer–autumn and negative biases during boreal winter–spring, although the easterly wind bias persists year round. This seasonally dependent bias is found to be caused by the model’s failure to reproduce the climatological seasonal wind reversal of the North American monsoon. During winter–spring, the observed easterly wind dominates, so the simulated stronger wind speed enhances surface evaporation and lowers SST. It is opposite when the observed wind turns to westerly during summer–autumn. An easterly wind bias, mainly evident in the lower troposphere, also occurs in the atmospheric model when the observed SST is prescribed, suggesting that it is of atmospheric origin. When the atmospheric model resolution is doubled in the CESM1, both SST and precipitation are improved in association with the reduced easterly wind bias. During boreal spring, when the double-ITCZ bias is most significant, the northern and southern ITCZ can be improved by 29.0% and 18.8%, respectively, by increasing the horizontal resolution in the CESM1. When dividing the 37 CMIP5 models into two groups on the basis of their horizontal resolutions, it is found that both the seasonally dependent biases over the northeastern Pacific and year-round biases over the southeastern Pacific are reduced substantially in the higher-resolution models, with improvement of ~30% in both regions during boreal spring. Close relationships between wind and precipitation biases over the northeastern and southeastern Pacific are also found among CMIP5 models.

Improved daily precipitation estimations were attempted using the parameter-elevation regressions on a parameter-elevation regression on independent slopes model (PRISM) with inverse-distance weighting (IDW) and a precipitation-masking algorithm for precipitation areas. The PRISM (PRISM_ORG) suffers two overestimation problems when the daily precipitation is estimated: overestimation of the precipitation intensity in mountainous regions and overestimation of the local precipitation areas. In order to solve the problem of overestimating the precipitation intensity, we used the IDW technique that employs the same input stations as those used in the PRISM regression (PRISM_IDW). A precipitation-masking algorithm that selectively masks the precipitation estimation grid points was additionally applied to the PRISM_IDW results (PRISM_MSK). For 6 months from March to August 2012, daily precipitation data were produced in a horizontal resolution of 1 km based on the above two experiments and PRISM_ORG. Afterwards, each experiment was evaluated for improvements. The monthly root mean squared errors (RMSEs) of PRISM_IDW and PRISM_MSK were reduced by 0.83 mm/day and 0.86 mm/day, respectively, compared to PRISM_ORG.

The precipitation over the Hengduan Mountains (HMs) during rainy seasons is investigated based on observations, reanalysis datasets, and 28 atmospheric general circulation models (AGCMs) from Coupled Model Intercomparison Project phase 5 (CMIP5). Most CMIP5 AGCMs generally capture two observed precipitation centers over the southwestern HMs and on the west side of Sichuan basin (WSSB), but their location, range, and magnitude vary with models. As the horizontal resolution increases, the details of simulated precipitation pattern are improved and closer to observation and reanalysis, as well as the increasing magnitude of precipitation over the southwestern HMs. However, the simulated precipitation on the WSSB is overestimated regardless of resolution. Mechanisms involved in resolution affecting precipitation pattern and biases of precipitation on the WSSB are explored. Representation of topography in AGCMs influences orographic effect, which contributes to simulations of both horizontal and vertical moisture flux convergence and further precipitation over the HMs. The biases of WSSB precipitation between reanalysis and AGCMs are attributed to the discrepancy in the vertical distribution of upward motions. The simulated upward motions can reach a higher level than reanalysis, and a spurious center of upward motions develops at 400 hPa due to the overestimation of circulation-precipitation feedback in AGCMs.

The coarse horizontal resolution global climate models (GCMs) have limitations in producing large biases over the mountainous region. Also, single model output or simple multi-model ensemble (SMME) outputs are associated with large biases. While predicting the rainfall extreme events, this study attempts to use an alternative modeling approach by using five different machine learning (ML) algorithms to improve the skill of North American Multi-Model Ensemble (NMME) GCMs during Indian summer monsoon rainfall from 1982 to 2009 by reducing the model biases. Random forest (RF), AdaBoost (Ada), gradient (Grad) boosting, bagging (Bag) and extra (Extra) trees regression models are used and the results from each models are compared against the observations. In simple MME (SMME), a wet bias of 20[Formula: see text]mm/day and an RMSE up to 15[Formula: see text]mm/day are found over the Himalayan region. However, all the ML models can bring down the mean bias up to [Formula: see text][Formula: see text]mm/day and RMSE up to 2[Formula: see text]mm/day. The interannual variability in ML outputs is closer to observation than the SMME. Also, a high correlation from 0.5 to 0.8 is found between in all ML models and then in SMME. Moreover, representation of RF and Grad is found to be best out of all five ML models that represent a high correlation over the Himalayan region. In conclusion, by taking full advantage of different models, the proposed ML-based multi-model ensemble method is shown to be accurate and effective.

To support the development of national adaptation policies and measures addressing climate change impacts over Italy, this work aims to analyze projected changes in mean temperatures and precipitations, and extreme events such as droughts and floods, highlighting some local trends in the different Italian regions that have been little considered to date. The investigations are made on the basis of a set of high-resolution Euro-CORDEX models (horizontal resolution 0.11°, about 12 km) to infer quantitative assessments about the danger of climate changes under three different Representative Concentration Pathways (RCPs): business as usual scenario, i.e., without a reduction in green-house gas emissions (RCP 8.5), medium stabilization scenario (RCP 4.5) and mitigation scenario (RCP 2.6). After filtering the models with limited performances in reconstructing the current climate, the multi-model climate change scenarios were characterized by comparing the ensemble mean values computed for the base-line period (1971–2000) with those elaborated for the short- (2021–2050), medium- (2041–2070) and long-term (2071–2100). Two WMO ETCCDI indices were considered to investigate climate extremes: Consecutive Dry Days and extreme precipitations. Despite some uncertainties (related to discrepancies among the models), drought conditions and extreme precipitations will likely exacerbate in the coming decades without mitigation (RCP 8.5). Such conditions will be less critical if partial mitigation actions will be undertaken (RCP 4.5) and are expected to be significantly reduced with decarbonization policies (RCP 2.6).

Effects of increase in horizontal resolution (from 2°, to 1° and 0.5°) in simulating Indian summer monsoon (ISM) in Community Atmosphere Model version 5 (CAM5) are reported herein. Various parameters — temperature, rainfall, winds, surface evaporation, precipitable water, specific humidity, and clouds — of the model simulations are examined with respect to observations {India Meteorological Department (IMD) and Tropical Rainfall Measuring Mission (TRMM) data for precipitation and Modern-Era Retrospective analysis for Research and Applications (MERRA) products for rest of the variables}. With increase in horizontal resolution overall improvement in majority of surface driven parameters like surface air temperature, surface specific humidity, and low-level winds is observed that highlights the importance of better representation of orography. Over the Indian subcontinent and surrounding oceans, rainfall is overestimated due to the overestimation of the large-scale precipitation in the model, which significantly gets alleviated with increase in resolution. Overall, seasonal mean features of ISM show significant improvements, which are likely to further improve by representing finer spatial features with increase in resolution. However, some aspects of its temporal variations — seasonal, intraseasonal, and diurnal variations — of monsoon rainfall do not show much improvement with increase in resolution, suggesting that traditional physics parameterizations do not work well at high resolution and hence there is a need for improvement in model physics.

This study investigates the effects of horizontal resolution, cumulus parameterization scheme (CPS), and probability forecasting on precipitation forecasts over the Korean Peninsula from 00 UTC 15 August to 12 UTC 14 September 2013, using the limited-area ensemble prediction system (LEPS) of the Korea Meteorological Administration. To investigate the effect of resolution, the control members of the LEPS with 1.5- and 3-km resolution were compared. Two 3-km experiments with and without the CPS were conducted for the control member, because a 3-km resolution lies within the gray zone. For probability forecasting, 12 ensemble members with 3-km resolution were run using the LEPS. The forecast performance was evaluated for both the whole study period and precipitation cases categorized by synoptic forcing. The performance of precipitation forecasts using the 1.5-km resolution was better than that using the 3-km resolution for both the total period and individual cases. The result of the 3-km resolution experiment with the CPS did not differ significantly from that without it. The 3-km ensemble mean and probability matching (PM) performed better than the 3-km control member, regardless of the use of the CPS. The PM complemented the defect of the ensemble mean, which better predicts precipitation regions but underestimates precipitation amount by averaging ensembles, compared to the control member. Further, both the 3-km ensemble mean and PM outperformed the 1.5-km control member, which implies that the lower performance of the 3-km control member compared to the 1.5-km control member was complemented by probability forecasting.

Extreme precipitation events, represented by the extreme hourly precipitation (EHP), often occur in the Tibetan Plateau and surrounding areas (TPS) as a result of the complex topography and unique geographical location of this region and can lead to large losses of human life. Previous studies have shown that the performance of extreme precipitation simulations can be improved by increasing the resolution of the model, although the mechanisms are not yet not clear. In this study, we firstly compared the most recent high-quality satellite precipitation product with station data from Nepal, which is located on the southern edge of the Tibetan Plateau. The results showed that the GPM dataset can reproduce extreme precipitation well and we therefore used these data as a benchmark for climate models of the TPS. We then evaluated the fidelity of global climate models in the representation of the boreal summer EHP in the TPS using datasets from the CMIP6 High-Resolution Model Intercomparison Project (HighResMIP). We used four global climate models with standard (about 100 km) and enhanced (up to 25 km) resolution configurations to simulate the EHP. The models with a standard resolution largely underestimated the intensity of EHP, especially over the southern edge of the Tibetan Plateau. The EHP can reach up to 50 mm h−1in the TPS, whereas the maximum simulated EHP was −1 for all the standard resolution models. The mean intensity of EHP is about 5.06 mm h−1 in the GPM satellite products, whereas it was −1 in standard resolution models. The skill of the simulation of EHP is significantly improved at increased horizontal resolutions. The high-resolution models with a horizontal resolution of 25 km can reproduce the geographical distribution of the intensity of EHP in the TPS. The intensity–frequency distribution of EHP also resembles that from GPM products, showing the same features up to 50 mm h−1, although it slightly overestimates heavy precipitation events. Finally, we propose possible physical linkages between the simulation of EHP and the impacts of the resolution of the model and physical processes. Phenomena over the Indian Ocean at different timescales and the diurnal variation of precipitation in the TPS are used to propose possible physical linkages as they may play an important part in the simulation of EHP in the TPS. Further analysis shows that an increase in the horizontal resolution helps to accurately reproduce the features of water vapor transport on days with extreme precipitation, the northward-propagating intraseasonal oscillation over the Indian and western Pacific Ocean monsoon regions in the boreal summer, the intensity and number of tropical cyclones over the southern Asian monsoon regions, and the peak time and amplitude of the diurnal cycle of precipitation. This increase in accuracy contributes to the improvements in the simulation of EHP in the TPS. This study suggests improvements to increase the horizontal resolution of the GCMs and lay a solid foundation for the accurate reproduction and prediction of EHP in the TPS.

We examine the resolution dependence of errors in extreme sub-daily precipitation in available high-resolution climate models. We find that simulated extreme precipitation increases as horizontal resolution increases but that appropriately constructed model skill metrics do not significantly change. We find little evidence that simulated extreme winter or summer storm processes significantly improve with the resolution because the model performance changes identified are consistent with expectations from scale dependence arguments alone. We also discuss the implications of these scale-dependent limitations on the interpretation of simulated extreme precipitation. This article is part of a discussion meeting issue ‘Intensification of short-duration rainfall extremes and implications for flash flood risks’.

For the first time for CORDEX-South Asia, a high-resolution regional earth system model (ROM) is adopted to assess the impact of horizontal resolution (0.22° and 0.11°) in simulating the Indian summer monsoon rainfall (ISMR) and the underlying spatiotemporal variability. ROM at both resolutions bears a close resemblance to observations in simulating the mean precipitation climatology compared to other regional climate models (RCMs) participated in CORDEX- South Asia. ROM shows substantial improvement relative to the ensemble mean of the RCMs included in CORDEX-South Asia. While comparing both simulations with observations, some systematic wet and dry bias over Central India (CI) and Northern Western Ghats is noticed. In general, the wet/dry bias over India is mainly associated with the overestimation/underestimation of the large-scale/convective component. Increasing horizontal resolution from 0.22° to 0.11° significantly adds value in simulating the JJAS mean precipitation by reducing the wet bias over western central India (WCI) and southern peninsular India and dry bias over eastern CI. The reduction in wet/dry bias is mainly associated with suppression/enhancement of the large scale/convective precipitation. This improvement in mean precipitation is partially due to the improved representation of the propagation of mesoscale systems such as boreal summer intraseasonal oscillation (eastward and northward). Despite the above improvements, the wet precipitation bias, particularly over WCI, persists. The weaker Findlater Jet associated with weaker land-ocean thermal contrast caused by the warm sea surface temperature (SST) bias over the western Arabian Sea (AS) suggests that AS moisture transport does not contribute to the wet bias over India. The wet bias is possibly associated with favourable atmospheric conditions (atmospheric instability).

This study evaluates the skill of the Weather Research and Forecasting (WRF) model to reproduce the variability of precipitation over the Central Andes of Chile and Argentina, a region characterized by complex topography. The simulation corresponds to a dynamical downscaling of ERA-Interim, in the period between 1996 and 2015, performed with two nested grids, at 9 and 3 km horizontal resolution. Precipitation data from 62 rain gauges from Chile and Argentina were used to evaluate the performance of WRF simulations carried out at the annual and warm-cold season analysis. The results of this study indicate that WRF at 9 and 3 km is able to reproduce the main characteristics of seasonal and interannual precipitation variability along the study area. On the windwards slopes of the Andes, however, WRF at 9 km presents a wet bias in relation to observation and WRF at 3 km. Additionally, WRF at 3 km achieves better performance of precipitation as elevation increases, most likely due to the better-resolved topography. To our knowledge, this is the first study that compares performance between nested domains on mountain areas that found a better match between the model and observations, as elevations increased.

We assess the effect of increasing horizontal resolution on simulated precipitation over South America in a climate model. We use atmosphere-only simulations, performed with HadGEM3-GC31 at three horizontal resolutions: N96 (∼130 km; 1.88∘×1.25∘), N216 (∼60 km; 0.83∘×0.56∘), and N512 (∼25 km; 0.35∘×0.23∘). We show that all simulations have systematic biases in annual mean and seasonal mean precipitation over South America (e.g. too wet over the Amazon and too dry in the northeast). Increasing horizontal resolution improves simulated precipitation over the Andes and northeast Brazil. Over the Andes, improvements from horizontal resolution continue to ∼25 km, while over northeast Brazil, there are no improvements beyond ∼60 km resolution. These changes are primarily related to changes in atmospheric dynamics and moisture flux convergence. Over the Amazon Basin, precipitation variability increases at higher resolution. We show that some spatial and temporal features of daily South American precipitation are improved at high resolution, including the intensity spectra of rainfall. Spatial scales of daily precipitation features are also better simulated, suggesting that higher resolution may improve the representation of South American mesoscale convective systems.

Precipitation over East Asia (EA) simulated in six experiments using the Met Office Unified Model (MetUM) is compared with observations and ERA-Interim reanalysis. These simulations are performed at resolutions of 200–40 km and with both atmosphere-only and air-sea coupled configurations. EA precipitation in MetUM is systematically overestimated, especially over southeastern EA and the Tibetan Plateau. Moisture sources for simulated and observed regional EA precipitation are traced using the Water Accounting Model-2layers (WAM-2layers) – a moisture tracking model that traces moisture sources using a combination of evaporation, atmospheric moisture and circulation. Biases in moisture sources are linked to biases in precipitation. For southeastern EA, positive precipitation biases are linked to errors in mid-latitude sources rather than tropical ocean sources. Increasing resolution reduces precipitation biases over the Tibetan Plateau. From the perspective of moisture source, this reduction comes from reduced remote moisture source that is blocked by the better representations of topography at higher resolution. Including coupling does not necessarily improve EA precipitation, however, coupling alters moisture sources. Because the effect of coupling on moisture source varies on location and sign, the collective impact on EA precipitation may not be noticeable. By using WAM-2layers, these changes in moisture sources can be attributed to changes in SST, circulation and associated evaporation. WAM-2layers can be a useful tool to identify model biases that cause biases in regional precipitation.

This study presents results from the Weather Research and Forecasting (WRF) model applied for climatological downscaling simulations over highly complex terrain along the Eastern Mediterranean. We sequentially downscale general circulation model results, for a mild and wet year (2003) and a hot and dry year (2010), to three local horizontal resolutions of 9, 3 and 1 km. Simulated near-surface hydrometeorological variables are compared at different time scales against data from an observational network over the study area comprising rain gauges, anemometers, and thermometers. The overall performance of WRF at 1 and 3 km horizontal resolution was satisfactory, with significant improvement over the 9 km downscaling simulation. The total yearly precipitation from WRF's 1 km and 3 km domains exhibited

This study examined the impacts of model horizontal resolution on vertical structures of convection in the tropics by performing sensitivity experiments with the NCAR CESM1. It was found that contributions to the total precipitation between top-heavy and bottom-heavy convection are different among various resolutions. A coarser resolution tends to produce a greater contribution from top-heavy convection and, as a result, stronger precipitation in the western Pacific ITCZ; while there is less contribution from bottom-heavy convection and weaker precipitation in the eastern Pacific ITCZ. In the western Pacific ITCZ, where the convection is dominated by a top-heavy structure, the stronger precipitation in coarser resolution experiments is due to changes in temperature and moisture profiles associated with a warmer environment (i.e., thermodynamical effect). In the eastern Pacific ITCZ, where the convection is dictated by a bottom-heavy structure, the stronger precipitation in finer resolution experiments comes from changes in convection structure (i.e., dynamic effect) which favors a greater contribution of bottom-heavy convection as the model resolution goes higher. The moisture budget analysis further suggested that the very different behavior in precipitation tendencies in response to model resolution changes between the western and eastern Pacific ITCZs are determined mainly by changes in convective structure rather than changes in convective strength. This study pointed out the importance of model spatial resolution in reproducing a reasonable contribution to the total precipitation between top-heavy and bottom-heavy structure of convection in the tropical Pacific ITCZs.

Changes in seasonal and diurnal precipitation types over South Korea during summer in the late twenty-first century (2081–2100) were projected under four RCP scenarios using the Regional Climate Model (RegCM4.0) with a horizontal resolution of 12.5 km. Two boundary conditions, ERA-Interim and HadGEM2-AO, were used to drive the RegCM4.0 (jointly named RG4_ERA and RG4_HG2, respectively). In general, the RegCM4.0 reproduces the spatial distribution of summer precipitation over Northeast Asia for the current climate (1989–2008) reasonably well. The RG4_HG2 shows larger dry biases over South Korea, when compared with observations, than does the RG4_ERA. These strong dry biases result from the underestimation of convective precipitation (CPR) and are particularly noticeable in late afternoons during July and August. It is related to the performance of HadGEM2-AO which simulated southwesterly winds weakly in that time. However, interestingly, the RG4_HG2 simulates similar increases in the contribution of CPR to total precipitation after mid-July, resulting in comparable performance in the reproduction of heavy precipitation. In the late twenty-first century, a significant increase (decrease) in CPR (NCPR) is generally projected over South Korea, and particularly under the RCP8.5. During June, the total precipitation is affected primarily by changes in NCPR under RCP2.6 and RCP6.0. After mid-July, increasing total precipitation is primarily caused by the distinct increases in CPR in the late afternoons; this pattern is particularly noticeable under RCP8.5, which is associated with more destabilized atmospheric conditions during July and August. Light and heavy precipitation are projected to decrease and increase, respectively, under RCP8.5.

Hindcast produced by a model used in a numerical model-based seasonal prediction system is an essential part in the operational seasonal prediction system. This paper is aimed at evaluating the performance of POAMA model from the climatological aspect. The data used in this research are obtained from three variants of POAMA m24 model. The results show that the annual cycle of climatological rainfall averaged over Indonesia is well simulated by POAMA m24, although there is a dry bias in the rainy season and a wet bias in the dry season. From those three variants, m24b model has a relatively low variation of bias against lead-time compared with the m24a and m24c models due to the implementation of flux correction scheme in m24b model. However, the performance of POAMA m24 model with a resolution of T24 is inferior to CFSv2 with a resolution of T126 in simulating the spatial pattern of rainfall over Indonesia. Beside model resolution, convection scheme used in a model also has significant influence. This can be seen from the resemblance between the spatial pattern of total rainfall and that of convective rainfall. Thus, the horizontal resolution of a model and a suitable convection scheme for Indonesian region are the two factors that must form important consideration in the development of Indonesian seasonal prediction system.

An attempt was made to evaluate the impact of assimilating Doppler Weather Radar (DWR) reflectivity together with Global Telecommunication System (GTS) data in the three-dimensional variational data assimilation (3DVAR) system of the Weather Research Forecast (WRF) model on rain storm prediction in Daqinghe basin of northern China. The aim of this study was to explore the potential effects of data assimilation frequency and to evaluate the outputs from different domain resolutions in improving the meso-scale NWP rainfall products. In this study, four numerical experiments (no assimilation, 1 and 6 h assimilation time interval with DWR and GTS at 1 km horizontal resolution, 6 h assimilation time interval with radar reflectivity, and GTS data at 3 km horizontal resolution) are carried out to evaluate the impact of data assimilation on prediction of convective rain storms. The results show that the assimilation of radar reflectivity and GTS data collectively enhanced the performance of the WRF-3DVAR system over the Beijing-Tianjin-Hebei region of northern China. It is indicated by the experimental results that the rapid update assimilation has a positive impact on the prediction of the location, tendency, and development of rain storms associated with the study area. In order to explore the influence of data assimilation in the outer domain on the output of the inner domain, the rainfall outputs of 3 and 1 km resolution are compared. The results show that the data assimilation in the outer domain has a positive effect on the output of the inner domain. Since the 3DVAR system is able to analyze certain small-scale and convective-scale features through the incorporation of radar observations, hourly assimilation time interval does not always significantly improve precipitation forecasts because of the inaccurate radar reflectivity observations. Therefore, before data assimilation, the validity of assimilation data should be judged as far as possible in advance, which can not only improve the prediction accuracy, but also improve the assimilation efficiency.

This study investigates the internal variability (IV) of a regional climate model, and considers the impacts of horizontal resolution and spectral nudging on the IV. A 16-member simulation ensemble was conducted using the Weather Research Forecasting model for three model configurations. Ensemble members included simulations at spatial resolutions of 50 and 12 km without spectral nudging and simulations at a spatial resolution of 12 km with spectral nudging. All the simulations were generated over the same domain, which covered much of North America. The degree of IV was measured as the spread between the individual members of the ensemble during the integration period. The IV of the 12 km simulation with spectral nudging was also compared with a future climate change simulation projected by the same model configuration. The variables investigated focus on precipitation and near-surface air temperature. While the IVs show a clear annual cycle with larger values in summer and smaller values in winter, the seasonal IV is smaller for a 50-km spatial resolution than for a 12-km resolution when nudging is not applied. Applying a nudging technique to the 12-km simulation reduces the IV by a factor of two, and produces smaller IV than the simulation at 50 km without nudging. Applying a nudging technique also changes the geographic distributions of IV in all examined variables. The IV is much smaller than the inter-annual variability at seasonal scales for regionally averaged temperature and precipitation. The IV is also smaller than the projected changes in air-temperature for the mid- and late twenty-first century. However, the IV is larger than the projected changes in precipitation for the mid- and late twenty-first century.

Simulations of the regional climate model REMO at 25 and 50 km (0.44° and 0.22°, respectively) grid spacing are evaluated over Central Africa for the period 1998–2010. The model's ability to simulate various aspects of the observed climate is assessed with a main focus on precipitation characteristics. The results show that the REMO model has a good skill to reproduce the main regional features of the mean climate fields as well as seasonal and annual cycles, although some biases still exist. The model captures the variability in precipitation anomalies between different events associated with El Nino/Southern Oscillation. Generally, the findings remain largely insensitive to the refinement of the horizontal resolution, especially when considering the regional scale. This points the fact that the model dynamical and physical setup may have to be adjusted when setting a focus on the Central African region, in order to better represent the climate mechanisms. It is further found that REMO is capable of capturing the basic features of climatic extremes and accurately represents the shape of the frequency distribution of daily precipitation data. Daily characteristics of precipitation are simulated much better by REMO at 25 km.

Inevitable drawbacks occur when the Asian monsoon is simulated at a horizontal resolution that is too low to resolve topographic effects and sub-grid processes. By comparing the Coupled Model Intercomparison Project (CMIP) phase 5 simulations conducted at higher (∼1°) and lower (∼3°) horizontal resolutions, this study attempted to extend understanding of potential resolution limits in simulating the East Asian presummer climate. April–May is found to have considerable resolution-dependent contrasts in position and strength of precipitation (spring rains and pre–Meiyu) among the simulations. The low-resolution models cannot simulate ascending vertical motion over the mountainous regions in the southern Tibetan Plateau and subtropical East Asia, which coincides with the overestimation of the strength of the lower-tropospheric westerly in 90°–105°E and of the downstream southerly in 110°–130°E. Because of the southerly wind bias, the East Asian presummer precipitation simulated in the low-resolution models is lower in amount and located farther north compared with the observed and high-resolution models. Despite distinct large-scale climatology between April and late May, the East Asian precipitation contrasts in these two periods are similar between the high- and low-resolution models, which could be possibly explained as a systematic response to resolution. Systematically less precipitation in the high-resolution model compared with the low-resolution model is also observable over the Maritime Continent, whereas a further connection to the East Asian precipitation simulation remains unclear. Regarding projected changes, the resolution-dependent contrasts are also considerable; the contrasts further suggest potential regional interactions such as between the subtropical South and East Asia (10°–20°N).

Rainfall-triggered landslides have posed significant threats to human lives and property each year in China. This paper proposed a meteorological-geotechnical early warning system GRAPES-LFM (GRAPES: Global and Regional Assimilation and PrEdiction System; LFM: Landslide Forecast Model), basing on the GRAPES model and the landslide predicting model TRIGRS (Transient Rainfall Infiltration and Grid-based Regional Slope-Stability Model) for predicting rainfall-triggered landslides. This integrated system is evaluated in Dehua County, Fujian Province, where typhoon Bilis triggered widespread landslides in July 2006. The GRAPES model runs in 5 km×5 km horizontal resolution, and the initial fields and lateral boundaries are provided by NCEP (National Centers for Environmental Prediction) FNL (Final) Operational Global Analysis data. Quantitative precipitation forecasting products of the GRAPES model are downscaled to 25 m×25 m horizontal resolution by bilinear interpolation to drive the TRIGRS model. Results show that the observed areas locate in the high risk areas, and the GRAPES-LFM model could capture about 74% of the historical landslides with the rainfall intense 30mm/h. Meanwhile, this paper illustrates the relationship between the factor of safety (FS) and different rainfall patterns. GRAPES-LFM model enables us to further develop a regional, early warning dynamic prediction tool of rainfall-induced landslides.

This article describes the main features of the Brazilian Global Atmospheric Model (BAM), analyses of its performance for tropical rainfall forecasting, and its sensitivity to convective scheme and horizontal resolution. BAM is the new global atmospheric model of the Center for Weather Forecasting and Climate Research [Centro de Previsão de Tempo e Estudos Climáticos (CPTEC)], which includes a new dynamical core and state-of-the-art parameterization schemes. BAM’s dynamical core incorporates a monotonic two-time-level semi-Lagrangian scheme, which is carried out completely on the model grid for the tridimensional transport of moisture, microphysical prognostic variables, and tracers. The performance of the quantitative precipitation forecasts (QPFs) from two convective schemes, the Grell–Dévényi (GD) scheme and its modified version (GDM), and two different horizontal resolutions are evaluated against the daily TRMM Multisatellite Precipitation Analysis over different tropical regions. Three main results are 1) the QPF skill was improved substantially with GDM in comparison to GD; 2) the increase in the horizontal resolution without any ad hoc tuning improves the variance of precipitation over continents with complex orography, such as Africa and South America, whereas over oceans there are no significant differences; and 3) the systematic errors (dry or wet biases) remain virtually unchanged for 5-day forecasts. Despite improvements in the tropical precipitation forecasts, especially over southeastern Brazil, dry biases over the Amazon and La Plata remain in BAM. Improving the precipitation forecasts over these regions remains a challenge for the future development of the model to be used not only for numerical weather prediction over South America but also for global climate simulations.

This study examines how characteristics of tropical cyclones (TCs) that are explicitly resolved in a global atmospheric model with horizontal resolution of approximately 28 km are projected to change in a warmer climate using bias-corrected sea-surface temperatures (SSTs). The impact of mitigating from RCP8.5 to RCP4.5 is explicitly considered and is compared with uncertainties arising from SST projections. We find a reduction in overall global TC activity as climate warms. This reduction is somewhat less pronounced under RCP4.5 than under RCP8.5. By contrast, the frequency of very intense TCs is projected to increase dramatically in a warmer climate, with most of the increase concentrated in the NW Pacific basin. Extremes of storm related precipitation are also projected to become more common. Reduction in the frequency of extreme precipitation events is possible through mitigation from RCP8.5 to RCP4.5. In general more detailed basin-scale projections of future TC activity are subject to large uncertainties due to uncertainties in future SSTs. In most cases these uncertainties are larger than the effects of mitigating from RCP8.5 to RCP4.5.

We investigated the performance of a convection-permitting regional climate model with respect to precipitation in the present climate around the southwestern oceanic region of Japan. The effects of explicit representation of convective processes without cumulus parameterization can be properly estimated by using a model domain without complex topography or convoluted coastlines. The amounts of annual and monthly precipitation and the frequencies of daily and hourly precipitation were well reproduced by the convection-permitting model with a 2-km grid spacing, and its performance was better than that of a model with a coarser mesh. In particular, the frequencies of hourly precipitation in the convection-permitting simulation matched the observed frequencies for precipitation intensities below 20 mm h−1. Above intensities of 20 mm h−1, however, the convection-permitting model tended to overestimate the frequency of hourly precipitation. To explore the mechanism of this overestimation of heavy hourly precipitation, the sensitivity of the frequency distribution of precipitation to the horizontal resolution was tested by changing the horizontal grid spacing of the model from 2 to 4 km and then 1.5 km. The results showed that the overestimation was increased when the horizontal resolution was coarser, owing to spurious grid-scale precipitation, which causes heavy precipitation to be highly concentrated in a single grid. This spurious grid-scale precipitation may be caused by insufficient representation of convective downdrafts in convection-permitting simulations by models with coarser resolutions.

High-impact Typhoon Morakot (2009) was investigated using a multiply nested regional tropical cyclone prediction model. In the numerical simulations, the horizontal grid spacing, cumulus parameterizations, and microphysical parameterizations were varied, and the sensitivity of the track, intensity, and quantitative precipitation forecasts (QPFs) was examined. With regard to horizontal grid spacing, it is found that convective-permitting (5 km) resolution is necessary for a reasonably accurate QPF, while little benefit is gained through the use of a fourth domain at 1.67-km horizontal resolution. Significant sensitivity of the track forecast was found to the cumulus parameterization, which impacted the model QPFs. In particular, the simplified Arakawa–Schubert parameterization tended to erroneously regenerate the remnants of Tropical Storm Goni to the southwest of Morakot, affecting the large-scale steering flow and the track of Morakot. Strong sensitivity of the QPFs to the microphysical parameterization was found, with the track and intensity showing little sensitivity. It is also found that Morakot’s accumulated precipitation was reasonably predictable, with the control simulation producing an equitable threat score of 0.56 for the 3-day accumulated precipitation using a threshold of 500 mm. This high predictability of precipitation is due in part to more predictable large-scale and topographic forcing.

High-resolution simulations of high precipitation events with the MESO-NH model are presented, and also used to verify that increasing horizontal resolution in zones of complex orography, such as in Madeira island, improve the simulation of the spatial distribution and total precipitation. The simulations succeeded in reproducing the general structure of the cloudy systems over the ocean in the four periods considered of significant accumulated precipitation. The accumulated precipitation over the Madeira was better represented with the 0.5 km horizontal resolution and occurred under four distinct synoptic situations. Different spatial patterns of the rainfall distribution over the Madeira have been identified.

An analysis of a 5-yr (from 1 January 2009 to 31 December 2013) free run of the superparameterized (SP) Climate Forecast System (CFS) version 2 (CFSv2) (SP-CFS), implemented for the first time at a spectral triangular truncation at wavenumber 62 (T62) atmospheric horizontal resolution, is presented. The SP-CFS simulations are evaluated against observations and traditional convection parameterized CFSv2 simulations at T62 resolution as well as at some higher resolutions. The metrics for evaluating the model performance are chosen in order to mainly address the improvement in systematic biases observed in the CFSv2 documented in earlier studies. While the primary focus of this work is on evaluating the improvement of the simulation of the Indian summer monsoon (ISM) by the SP-CFS model, some results are also presented within the context of the global climate. The SP-CFS significantly reduces the dry bias of precipitation over the Indian subcontinent and better captures the monsoon intraseasonal oscillation (MISO) modes. SP-CFS also improves the northward and eastward propagation of high- and low-frequency modes of ISM. Compared to CFSv2, the SP-CFS model simulates improved convectively coupled equatorial waves; better temperature structures both spatially and vertically, leading to a significantly improved relative distribution of variance for the synoptic disturbances and low-frequency tropical intraseasonal oscillations (ISOs). This analysis of the development of SP-CFS is particularly important as it shows promise for improving the cloud process representation through an SP framework and is able to improve the mean as well as intraseasonal characteristics of CFSv2 within the context of the ISM.

On 10 October 2018 an intense storm, characterized by heavy rainfall, hit the Sardinia island, reaching a peak of 452 mm of rain measured in 24 h. Among others, two particularly intense phases were registered between 3 and 6 UTC (Universal Coordinated Time), and between 18 and 24 UTC. The forecast of this case study is challenging because the precipitation was heavy and localized. In particular, the meteorological model used in this paper, provides a good prediction only for the second period over the eastern part of the Sardinia island. In this work, we study the impact of lightning data assimilation and horizontal grid resolution on the Very Short-term Forecast (VSF, 3 and 1 h) for this challenging case, using the RAMS@ISAC meteorological model. The comparison between the 3 h VSF control run and the simulations with lightning data assimilation shows the considerable improvement given by lightning data assimilation, especially for the precipitation that occurred in the eastern part of the island. Reducing the VSF range to 1 h, resulted in higher model performance with a good precipitation prediction over eastern and south-central Sardinia. In addition, the comparison between simulated and observed reflectivity shows an important improvement of simulations with lightning data assimilation compared to the control forecast. However, simulations assimilating lightning overestimated the precipitation in the last part of the day. The increasing of the horizontal resolution to 2 km grid spacing reduces the false alarms and improves the model performance.

A test of sensitivity to the model grid spacing for extreme rainfall simulation is carried out for the tropical island of La Réunion, which holds several world records of precipitation. An extreme rain event occurring during the moist season in 2011 is selected to study the numerical model behavior at four horizontal resolutions: 4 km, 2 km, 1 km and 500 m. The assessment based on raingauge network shows that the performance of daily rain simulation increases as reducing the model grid spacing from 4 km to 1 km. The spatial variability of 24 h rainfall is well captured by the simulation at 1 km and 500 m resolution. However, refining the resolution from 1 km to 500 m has little impact on the model performance compared to the 1 km run. Diagnosis analysis and numerical experiment reveal that only the 1 km and 500 m grid spacings are able to simulate a cold pool located near the coastal area of the island. This cold pool triggers the thermal lifting and creates convergence between the prevailing moist flow and offshore land breeze. The observed precipitation, air temperature and wind get good agreements with these simulated features. However, this cold pool is missed in the 4 km and 2 km simulations. Our study highlights the important role of air mixing with microphysical processes at 1 km scale in simulating such intense precipitations.

International audience; Most climate models do not explicitly model urban areas and at best describe them as rock covers. Nonetheless, the very high resolutions reached now by the regional climate models may justify and require a more realistic parameterization of surface exchanges between urban canopy and atmosphere. To quantify the potential impact of urbanization on the regional climate, and evaluate the benefits of a detailed urban canopy model compared with a simpler approach, a sensitivity study was carried out over France at a 12-km horizontal resolution with the ALADIN-Climate regional model for 1980-2009 time period. Different descriptions of land use and urban modeling were compared, corresponding to an explicit modeling of cities with the urban canopy model TEB, a conventional and simpler approach representing urban areas as rocks, and a vegetated experiment for which cities are replaced by natural covers. A general evaluation of ALADIN-Climate was first done, that showed an overestimation of the incoming solar radiation but satisfying results in terms of precipitation and near-surface temperatures. The sensitivity analysis then highlighted that urban areas had a significant impact on modeled near-surface temperature. A further analysis on a few large French cities indicated that over the 30 years of simulation they all induced a warming effect both at daytime and nighttime with values up to + 1.5 °C for the city of Paris. The urban model also led to a regional warming extending beyond the urban areas boundaries. Finally, the comparison to temperature observations available for Paris area highlighted that the detailed urban canopy model improved the modeling of the urban heat island compared with a simpler approach.

Using regional climate-model runs with a horizontal resolution of 5.5 km for two future scenarios and two time slices (representative concentration pathway [RCP] 4.5 and 8.5; 2031–2050 and 2081–2100) relative to a historical period (1991–2010), we study the climate change for the Qeqqata municipality in general and for Kangerlussuaq in particular. The climate-model runs are validated against observations of temperature and surface mass balance and a reanalysis simulation with the same model setup as the scenario runs, providing high confidence in the results. Clear increases in temperature and precipitation for the end of the 21st century are shown, both on and off the ice sheet, with an off–ice sheet mean annual temperature increase of 2.5–3°C for the RCP4.5 scenario and 4.8–6.0°C for the RCP8.5 scenario, and for precipitation an increase of 20–30% for the RCP4.5 scenario and 30–80% for the RCP8.5 scenario. Climate analogs for Kangerlussuaq for temperature and precipitation are provided, indicating that end-of-the-century Kangerlussuaq mean annual temperature is comparable with temperatures for the south of Greenland today. The extent of glacial retreat is also estimated for the Qeqqata municipality, suggesting that most of the ice caps south of the Kangerlussuaq fjord will be gone before the end of this century. Furthermore, the high-resolution runs are compared with an ensemble of six models run at a 50 km resolution, showing the need for high-resolution model simulations over Greenland.

In this study, the impact of global climate change on the temperature and precipitation regime over the island of Cyprus has been investigated. The analysis is based on daily output from a regional climate model (RCM) at a high horizontal resolution (25 km) produced within the framework of the EU-funded ENSEMBLES project. The control run represents the base period 1961–1990 and is used here as reference for comparison with future predictions. Two future periods are studied, 2021–2050 and 2071–2100. For the study area and over the study period, an analysis of the changes associated with the temperature regime and the hydrological cycle, such as mean precipitation and drought duration, is presented. Variations in the mean annual and seasonal rainfall are presented. Changes in the number of hot days/warm nights as well as drought duration are also discussed. These changes should be very important to assess future possible water shortages over the island and to provide a basis for associated impacts on the agricultural sector.

Dynamic downscaling is the best way to get high resolution quantitative precipitation forecasts. The Weather Research and Forecasting Model (WRF) was utilized as a dynamic downscaling tool in this study. The influence of the horizontal resolutions and the model domains on precipitation forecasts has been analysed to establish an optimized dynamic downscaling scheme. Three precipitation events over Xijiang basin, China, were simulated with different horizontal resolutions and domains. The results indicate that both the horizontal resolution and model domain have an influence on the precipitation forecast. However, the correlation between high precipitation forecast accuracy and the high horizontal resolution or the large model domain were not very strong. Comprehensive consideration of the results shows that the accuracy of forecast is best when the horizontal resolution is 20-km. Although the model domain size has no significant influence on the precipitation forecast, a larger domain may improve the stability of forecasting.