The climate change projections of the Intergovernmental Panel on Climate Change are based on scenarios for future emissions, but these are not statistically-based and do not have a full probabilistic interpretation. Raftery et al. (Nat Clim Change 7:637-641, 2017) and Liu and Raftery (Commun Earth Environ 2:1-10, 2021) developed probabilistic forecasts for global average temperature change to 2100, but these do not give forecasts for specific parts of the globe. Here we develop a method for probabilistic long-term spatial forecasts of local average annual temperature change, combining the probabilistic global method with a pattern scaling approach. This yields a probability distribution for temperature in any year and any part of the globe in the future. Out-of-sample predictive validation experiments show the method to be well calibrated. Consistent with previous studies, we find that for long-term temperature changes, high latitudes warm more than low latitudes, continents more than oceans, and the Northern Hemisphere more than the Southern Hemisphere, except for the North Atlantic. There is a 5% chance that the temperature change for the Arctic would reach 16 °C. With probability 95%, the temperature of North Africa, West Asia and most of Europe will increase by at least 2 °C. We find that natural variability is a large part of the uncertainty in early years, but this declines so that by 2100 most of the overall uncertainty comes from model uncertainty and uncertainty about future emissions.

During the austral winter (June-August) of 2021, the meteorological services of Brazil, Argentina, Peru, Paraguay, Bolivia, and Chile all issued forecasts for unusually cold conditions. Record-low minimum temperatures and cold spells were documented, including one strong cold wave episode that affected 5 countries. In this study, we define a  as a period in which daily maximum and minimum air temperatures are below the corresponding climatological 10th percentile for three or more consecutive days. The intense cold wave event in the last week of June, 2021, resulted in record-breaking minimum daily temperatures in several places in central South America and Chile. Several locations had temperatures about 10 °C below average, central South America had freezing conditions, and southern Brazil even saw snow. The cold air surge was characterized by an intense upper-air trough located close to 35° S and 70° W. The southerly flow to the west of this trough brought very cold air northward into subtropical and tropical South America. A northward flow between the lower-level cyclonic and anticyclonic perturbations caused the intense southerly flow between the upper-level ridge and trough. This condition facilitated the inflow of near-surface cold air from southern Argentina into southeastern Brazil and tropical South America east of the Andes. In the city of São Paulo, the cold wave caused the death of 13 homeless people from hypothermia. Frost and snow across southern and southeastern Brazil caused significant damage to coffee, sugarcane, oranges, grapes, and other fruit and vegetable crops. Wine and coffee production fell, the latter by 30%, and prices of food and commodities in the region rose.

The Arctic Ocean's Beaufort Gyre (BG) is a wind-driven reservoir of relatively fresh seawater, situated beneath time-mean anticyclonic atmospheric circulation, and is covered by mobile pack ice for most of the year. Liquid freshwater accumulation in and expulsion from this gyre is of critical interest due to its potential to affect the Atlantic meridional overturning circulation and due to the importance of freshwater in modulating vertical fluxes of heat, nutrients and carbon in the ocean, and exchanges of heat and moisture with the atmosphere. Here, we investigate the hypothesis that wind-driven sea ice transport into/from the BG region influences the freshwater content of the gyre and its variability. To test this hypothesis, we use the results of a coordinated climate response function experiment with four ice-ocean models, in combination with targeted experiments using a regional setup of the MITgcm, in which we rotate the surface wind forcing vectors (thereby changing the ageostrophic component of these winds). Our results show that, via an effect on the net thermodynamic growth rate, anomalies in sea ice transport into the BG affect liquid freshwater adjustment. Specifically, increased ice import increases freshwater retention in the gyre, whereas ice export decreases freshwater in the gyre. Our results demonstrate that uncertainty in the ageostrophic component of surface winds, and in the dynamic sea ice response to these winds, has important implications for ice thermodynamics and freshwater. This sensitivity may explain some of the observed inter-model spread in simulations of Beaufort Gyre freshwater and its adjustment in response to wind forcing.

Climate change induces a myriad of effects which influences the global tropical cyclone (TC) genesis frequency. Here we explore how North Atlantic and Western Pacific TCs are affected under climate change using a present-day and a future (1% pCO2 scenario) ensemble of high resolution simulations. We find that the number of TCs decreases () in the North Atlantic but increases () in the Western Pacific. Part of these opposing variations are linked to differences in the ocean's meridional overturning circulation, which gives rise to a different sea surface temperature response and air-sea fluxes between the two basins. The results show the important role of oceanic climate change on TC response.

The response of lightning to a changing climate is not fully understood. Historic trends of proxies known for fostering convective environments suggest an increase of lightning over large parts of Europe. Since lightning results from the interaction of processes on many scales, as many of these processes as possible must be considered for a comprehensive answer. Recent achievements of decade-long seamless lightning measurements and hourly reanalyses of atmospheric conditions including cloud micro-physics combined with flexible regression techniques have made a reliable reconstruction of cloud-to-ground lightning down to its seasonally varying diurnal cycle feasible. The European Eastern Alps and their surroundings are chosen as reconstruction region since this domain includes a large variety of land-cover, topographical and atmospheric circulation conditions. The most intense changes over the four decades from 1980 to 2019 occurred over the high Alps where lightning activity doubled in the 2010 s compared to the 1980 s. There, the lightning season reaches a higher maximum and starts one month earlier. Diurnally, the peak is up to 50% stronger with more lightning strikes in the afternoon and evening hours. Signals along the southern and northern alpine rim are similar but weaker whereas the flatlands surrounding the Alps have no significant trend.

Heatwaves (HWs) are expected to increase both in duration and intensity in the next decades, but little is known about their synoptic and mesoscalar behavior, which is especially important in mid-latitude regions. Most climate research has focused on temperature analysis to characterize HWs. We propose that a combination of temperature and synoptic patterns is a better way to define and understand HWs because including atmospheric circulation patterns provides information about different HW structures that can irregularly affect the territory, and illustrate this approach at the regional and urban scales using the Iberian Peninsula and the Metropolitan Area of Barcelona as case studies. We first select HW events from 1950 to 2020 and apply a multivariate analysis to identify synoptic patterns based on mean sea level pressure, geopotential height at 500 hPa, and maximum daily 2 m temperature. The results indicate that four synoptic patterns reproduce at least 50% of the variance in HWs, namely, "stationary and stable", "dynamic and advective", "stationary and advective", and "dynamic, advective and undulated". Next, we apply the analysis to the Representative Concentration Pathway future scenarios (RCPs) 4.5 and 8.5 from the Coordinated Regional Climate Downscaling Experiment (CORDEX) to determine how these synoptic trends can change in the future. The analysis shows that the four synoptic patterns continue to explain 55 to 60% of the variance in HWs. Future HW events will be characterized by an increase in geopotential height at 500 hPa due to the northward shift of the anticyclonic ridge. This is especially true for RCP8.5, which simulates business as usual incrementing fossil fuel use and additionally shows an increase in atmospheric dynamism in north advections from all directions in comparison with RCP4.5. These findings point to the importance of considering the geopotential height in HW prediction, as well as the direction of advections.

The Earth System is warming due to anthropogenic greenhouse gas emissions which increases the risk of passing a tipping point in the Earth System, such as a collapse of the Atlantic Meridional Overturning Circulation (AMOC). An AMOC weakening can have large climate impacts which influences the marine and terrestrial carbon cycle and hence atmospheric pCO . However, the sign and mechanism of this response are subject to uncertainty. Here, we use a state-of-the-art Earth System Model, the Community Earth System Model v2 (CESM2), to study the atmospheric pCO response to an AMOC weakening under low (SSP1-2.6) and high (SSP5-8.5) emission scenarios over the years 2015-2100. A freshwater flux anomaly in the North Atlantic strongly weakens the AMOC, and we simulate a weak positive pCO response of 0.45 and 1.3 ppm increase per AMOC decrease in Sv for SSP1-2.6 and SSP5-8.5, respectively. For SSP1-2.6 this response is driven by both the oceanic and terrestrial carbon cycles, whereas in SSP5-8.5 it is solely the ocean that drives the response. However, the spatial patterns of both the climate and carbon cycle response are similar in both emission scenarios over the course of the simulation period (2015-2100), showing that the response pattern is not dependent on cumulative CO emissions up to 2100. Though the global atmospheric pCO response might be small, locally large changes in both the carbon cycle and the climate system occur due to the AMOC weakening, which can have large detrimental effects on ecosystems and society.

This study provides extended seasonal predictions for the Upper Colorado River Basin (UCRB) precipitation in boreal spring using an artificial neural network (ANN) model and a stepwise linear regression model, respectively. Sea surface temperature (SST) predictors are developed taking advantage of the correlation between the precipitation and SST over three ocean basins. The extratropical North Pacific has a higher correlation with the UCRB spring precipitation than the tropical Pacific and North Atlantic. For the ANN model, the Pearson correlation coefficient between the observed and predicted precipitation exceeds 0.45 (-value < 0.01) for a lead time of 12 months. The mean absolute percentage error (MAPE) is below 20% and the Heidke skill score (HSS) is above 50%. Such long-lead prediction skill is probably due to the UCRB soil moisture bridging the SST and precipitation. The stepwise linear regression model shows similar prediction skills to those of ANN. Both models show prediction skills superior to those of an autoregression model (correlation < 0.10) that represents the baseline prediction skill and those of three of the North American Multi-Model Ensemble (NMME) forecast models. The three NMME models exhibit different skills in predicting the precipitation, with the best skills of the correlation ~ 0.40, MAPE < 25%, and HSS > 40% for lead times less than 8 months. This study highlights the advantage of oceanic climate signals in extended seasonal predictions for the UCRB spring precipitation and supports the improvement of the UCRB streamflow prediction and related water resource decisions.

We formulate a new conceptual model, named "2", to describe global ocean heat uptake, as simulated by atmosphere-ocean general circulation models (AOGCMs) forced by increasing atmospheric CO, as a function of global-mean surface temperature change and the strength of the Atlantic meridional overturning circulation (AMOC, ). 2 has two routes whereby heat reaches the deep ocean. On the basis of circumstantial evidence, we hypothetically identify these routes as low- and high-latitude. In low latitudes, which dominate the global-mean energy balance, heat uptake is temperature-driven and described by the two-layer model, with global-mean as the temperature change of the upper layer. In high latitudes, a proportion (about 14%) of the forcing is taken up along isopycnals, mostly in the Southern Ocean, nearly like a passive tracer, and unrelated to . Because the proportion depends linearly on the AMOC strength in the unperturbed climate, we hypothesise that high-latitude heat uptake and the AMOC are both affected by some characteristic of the unperturbed global ocean state, possibly related to stratification. 2 can explain several relationships among AOGCM projections, some found in this work, others previously reported:  Ocean heat uptake efficiency correlates strongly with the AMOC.  Global ocean heat uptake is not correlated with the AMOC.  Transient climate response (TCR) is anticorrelated with the AMOC.   projected for the late twenty-first century under high-forcing scenarios correlates more strongly with the effective climate sensitivity than with the TCR.

Europe experienced severe heat waves during the last decade, which impacted ecological and societal systems and are likely to increase under projected global warming. A better understanding of pre-industrial warm-season changes is needed to contextualize these recent trends and extremes. Here, we introduce a network of 352 living and relict larch trees ( Mill. from the Matter and Simplon valleys in the Swiss Alps to develop a maximum latewood density (MXD) chronology calibrating at r = 0.8 (p > 0.05, 1901-2017 CE) against May-August temperatures over Western Europe. Machine learning is applied to identify historical wood samples aligning with growth characteristics of sites from elevations above 1900 m asl to extend the modern part of the chronology back to 881 CE. The new Alpine record reveals warmer conditions in the tenth century, followed by an extended cold period during the late Medieval times, a less-pronounced Little Ice Age culminating in the 1810s, and prolonged anthropogenic warming until present. The Samalas eruption likely triggered the coldest reconstructed summer in Western Europe in 1258 CE (-2.32 °C), which is in line with a recently published MXD-based reconstruction from the Spanish Pyrenees. Whereas the new Alpine reconstruction is potentially constrained in the lowest frequency, centennial timescale domain, it overcomes variance biases in existing state-of-the-art reconstructions and sets a new standard in site-control of historical samples and calibration/ verification statistics.

We present a new approach to identify severe hailstorms in a convection-permitting climate model, and build a climatology of severe hail potential in Europe using an ingredients-based approach based on a 20-year long hindcast simulation. Severe hail in Europe occurs mostly in southern regions (up to 40 times a year per 10,000 km around Northern Italy), and from May to August. It peaks from afternoon to evening hours on land, whilst sea areas are prone to hail at any time of the day. The Mediterranean Sea experiences severe hailstorms mostly in autumn: the central Mediterranean has the highest frequency among all regions studied, and may be considered as an unknown alley for hailstorms in Europe. Results derived from the high-resolution model are in very good agreement with existing hail climatologies constructed from observations, including the fine scale spatial variation. We conclude that our approach provides a reliable proxy for studying future changes in severe hail in convection-permitting simulations.

This study investigates winter polynyas in the southern Ross Sea, Antarctica where several polynyas are known to form. Coastal polynyas are areas of lower sea ice concentration and/or thickness along the coast that are otherwise surrounded by more extensive, thicker sea ice pack. Polynyas are also locations where organisms can exploit both the ice substrate and pelagic resources. Using a self organizing map algorithm, we identify polynya events in the Community Earth System Model Version 2 Large Ensemble (CESM2-LE). The neural network algorithm is able to identify polynya events without imposing an ice concentration or thickness threshold, as is often done when identifying polynyas. The CESM2-LE produces a wintertime polynya feature comparable in size and location to the Ross Sea polynya, and during polynya events there are large turbulent heat fluxes and export of sea ice from the Ross Sea. In the CESM2-LE polynya event frequency is projected to decrease sharply in the later twentyfirst century, leading to increasing sea ice concentrations and thicknesses in the region. The drivers of the polynya frequency decline are likely both large scale circulation changes and local atmosphere and ocean feedbacks. If declines in wintertime polynya frequency over the twentyfirst century do occur they may impact Antarctic Bottom Water formation and local net primary productivity. Thus, better understanding potential local and unexpected sea ice changes in the Ross Sea is important for both assessing climate system impacts and ecological impacts on the Ross Sea ecosystem, which is currently protected by an internationally recognized marine protected area.

Subseasonal-to-seasonal (S2S) prediction is a global effort to forecast the state of the atmosphere and ocean with lead times between two weeks and a season. This study explores the feasibility of S2S prediction of the ocean using a variety of tools including statistical analysis, a statistical-dynamical mixed layer model, and a regional, high-resolution ocean circulation model based on physical principles. Ocean predictability on S2S timescales is analyzed by compositing winter sea surface temperature (SST) anomalies in the North Atlantic with respect to the state of the Madden-Julian Oscillation (MJO). It is found that statistically significant, large-scale SST changes, particularly along the eastern seaboard of North America, can be related to the MJO. This signal is shown to be driven by anomalous air-sea heat fluxes caused by atmospheric perturbations in response to the MJO. The high-resolution model of the Gulf of Maine and Scotian Shelf is used to downscale the mean ocean response to the MJO. The model is able to capture the observed relationship between the MJO and SST in the northwest Atlantic. It is also shown that the anomalous atmospheric circulation in response to the MJO leads to anomalous upwelling on the Scotian Shelf. Overall, this study demonstrates that it is feasible, and of value, to use regional ocean models for S2S prediction.

We investigate historical simulations of relevant components of the Arctic energy and water budgets for 39 Coupled Model Intercomparison Project Phase 6 (CMIP6) models and validate them against observation-based estimates. We look at simulated seasonal cycles, long-term averages and trends of lateral transports and storage rates in atmosphere and ocean as well as vertical fluxes at top-of-atmosphere and the surface. We find large inter-model spreads and systematic biases in the representation of annual cycles and long-term averages. Surface freshwater fluxes associated with precipitation and evaporation as well as runoff from Arctic lands tend to be overestimated by most CMIP6 models and about two thirds of the analysed models feature an early timing bias of one month in the runoff cycle phase, related to an early snow melt bias and the lack of realistic river routing schemes. Further, large biases are found for oceanic volume transports, partly because data required for accurate oceanic transport computations has not been archived. Biases are also present in the simulated energy budget components. The net vertical energy flux out of the ocean at the Arctic surface as well as poleward oceanic heat transports are systematically underestimated by all models. We find strong anti-correlation between average oceanic heat transports and mean sea ice cover, atmospheric heat transports, and also the long-term ocean warming rate. The latter strongly suggests that accurate depiction of the mean state is a prerequisite for realistic projections of future warming of the Arctic. Our diagnostics also provide useful process-based metrics for model selection to constrain projections.

Glacial-interglacial cycles constitute large natural variations in Earth's climate. The Mid-Pleistocene Transition (MPT) marks a shift of the dominant periodicity of these climate cycles from to  kyr. Recently, it has been suggested that this shift resulted from a gradual increase in the internal period (or equivalently, a decrease in the natural frequency) of the system. As a result, the system would then have locked to ever higher multiples of the external forcing period. We find that the internal period is sensitive to the strength of positive feedbacks in the climate system. Using a carbon cycle model in which feedbacks between calcifier populations and ocean alkalinity mediate atmospheric CO we simulate stepwise periodicity changes similar to the MPT through such a mechanism. Due to the internal dynamics of the system, the periodicity shift occurs up to millions of years after the change in the feedback strength is imposed. This suggests that the cause for the MPT may have occurred a significant time before the observed periodicity shift.

Recent variability in West African monsoon rainfall (WAMR) has been shown to be influenced by multiple ocean-atmosphere modes, including the El Niño Southern Oscillation, Atlantic Multidecadal Oscillation and the Interdecadal Pacific Oscillation. How these modes will change in response to long term forcing is less well understood. Here we use four transient simulations driven by changes in orbital forcing and greenhouse gas concentrations over the past 6000 years to examine the relationship between West African monsoon rainfall multiscale variability and changes in the modes associated with this variability. All four models show a near linear decline in monsoon rainfall over the past 6000 years in response to the gradual weakening of the interhemispheric gradient in sea surface temperatures. The only indices that show a long-term trend are those associated with the strengthening of the El Niño Southern Oscillation from the mid-Holocene onwards. At the interannual-to-decadal timescale, WAMR variability is largely influenced by Pacific-Atlantic - Mediterranean Sea teleconnections in all simulations; the exact configurations are model sensitive. The WAMR interannual-to-decadal variability depicts marked multi-centennial oscillations, with La Niña/negative Pacific Decadal Oscillation and a weakening and/or poleward shift of subtropical high-pressure systems over the Atlantic favoring wet WAMR anomalies. The WAMR interannual-to-decadal variability also depicts an overall decreasing trend throughout the Holocene that is consistent among the simulations. This decreasing trend relates to changes in the North Atlantic and Gulf of Guinea Sea Surface Temperature variability.

The interannual variability of the Equatorial Eastern Indian Ocean (EEIO) is highly relevant for the climate anomalies on adjacent continents and affects global teleconnection patterns. Yet, this is an area where seasonal forecasting systems exhibit large errors. Here we investigate the reasons for these errors in the ECMWF seasonal forecasting system SEAS5 using tailored diagnostics and a series of numerical experiments. Results indicate that there are two fundamental and independent sources of forecast errors in the EEIO. The first one is of atmospheric nature and is largely related with too strong and stable easterly atmospheric circulation present in the equatorial Indian Ocean. This induces an easterly bias which leaves the coupled model predominantly in a state with a shallow thermocline and cold SSTs in the EEIO. The second error is of oceanic origin, associated with a too shallow thermocline, which enhances the SST errors arising from errors in the wind. Ocean initial conditions, which depend on both the quality of the assimilation and the ocean model, play an important role in this context. Nevertheless, it is found that the version of the ocean model used for the forecast can also play a non-negligible role at the seasonal time scales, by amplifying or damping the subsurface errors in the initial conditions. Errors in the EEIO are regime-dependent, having different causes in the warm (deep thermocline) regime with strong atmospheric convection and in the cold (shallow thermocline) regime. Errors also exhibit decadal variations, which challenges the calibration methods used in seasonal forecasts.

Freezing precipitation has major consequences for ground and air transportation, the health of citizens, and power networks. Previous studies using coarse resolution climate models have shown a northward migration of freezing rain in the future. Increased model resolution can better define local topography leading to improved representation of conditions that are favorable for freezing rain. The goal of this study is to examine the climatology and characteristics of future freezing rain events using very-high resolution climate simulations. Historical and pseudo-global warming simulations with a 4-km horizontal grid length were used and compared with available observations. Simulations revealed a northerly shift of freezing rain occurrence, and an increase in the winter. Freezing rain was still shown to occur in the Saint-Lawrence River Valley in a warmer climate, primarily due to stronger wind channeling. Up to 50% of the future freezing rain events also occurred in present day climate within 12 h of each other. In northern Maine, they are typically shorter than 6 h in current climate and longer than 6 h in warmer conditions due to the onset of precipitation during low-pressure systems occurrences. The occurrence of freezing rain also locally increases slightly north of Québec City in a warmer climate because of freezing rain that is produced by warm rain processes. Overall, the study shows that high-resolution regional climate simulations are needed to study freezing rain events in warmer climate conditions, because high horizontal resolutions better define small-scale topographic features and local physical mechanisms that have an influence on these events.

Atmospheric rivers (ARs) reach High Mountain Asia (HMA) about 10 days per month during the winter and spring, resulting in about 20 mm day of precipitation. However, a few events may exceed 100 mm day, providing most of the total winter precipitation and increasing the risk of precipitation-triggered landslides and flooding, particularly when the height of the height of the 0 C isotherm, or freezing level is above-average. This study shows that from 1979 to 2015, integrated water vapor transport (IVT) during ARs that reach Western HMA has increased 16% while the freezing level has increased up to 35 m. HMA ARs that have an above-average freezing level result in 10-40% less frozen precipitation compared to ARs with a below-average freezing level. To evaluate the importance of these trends in the characteristics of ARs, we investigate mesoscale processes leading to orographic precipitation using Advanced Weather Research and Forecasting (ARW-WRF) simulations at 6.7 km spatial resolution. We contrast two above- and below- average freezing level AR events with otherwise broadly similar characteristics and show that with a 50-600 m increase in freezing level, the above-average AR resulted in 10-70% less frozen precipitation than the below-average event. This study contributes to a better understanding of climate change-related impacts within HMA's hydrological cycle and the associated hazards to vulnerable communities living in the region.

High-frequency precipitation variance is calculated in 12 different free-running (non-data-assimilative) coupled high resolution atmosphere-ocean model simulations, an assimilative coupled atmosphere-ocean weather forecast model, and an assimilative reanalysis. The results are compared with results from satellite estimates of precipitation and rain gauge observations. An analysis of irregular sub-daily fluctuations, which was applied by Covey et al. (Geophys Res Lett 45:12514-12522, 2018. 10.1029/2018GL078926) to satellite products and low-resolution climate models, is applied here to rain gauges and higher-resolution models. In contrast to lower-resolution climate simulations, which Covey et al. (2018) found to be lacking with respect to variance in irregular sub-daily fluctuations, the highest-resolution simulations examined here display an irregular sub-daily fluctuation variance that lies closer to that found in satellite products. Most of the simulations used here cannot be analyzed via the Covey et al. (2018) technique, because they do not output precipitation at sub-daily intervals. Thus the remainder of the paper focuses on frequency power spectral density of precipitation and on cumulative distribution functions over time scales (2-100 days) that are still relatively "high-frequency" in the context of climate modeling. Refined atmospheric or oceanic model grid spacing is generally found to increase high-frequency precipitation variance in simulations, approaching the values derived from observations. Mesoscale-eddy-rich ocean simulations significantly increase precipitation variance only when the atmosphere grid spacing is sufficiently fine (< 0.5°). Despite the improvements noted above, all of the simulations examined here suffer from the "drizzle effect", in which precipitation is not temporally intermittent to the extent found in observations.

Climate science has long explored whether higher resolution regional climate models (RCMs) provide improved simulation of regional climates over global climate models (GCMs). The advent of convective-permitting RCMs (CPRCMs), where sufficiently fine-scale grids allow explicitly resolving rather than parametrising convection, has created a clear distinction between RCM and GCM formulations. This study investigates the simulation of tropical-extratropical (TE) cloud bands in a suite of pan-South America convective-permitting Met Office Unified Model (UM) and Weather Research and Forecasting (WRF) climate simulations. All simulations produce annual cycles in TE cloud band frequency within 10-30% of observed climatology. However, too few cloud band days are simulated during the early summer (Nov-Dec) and too many during the core summer (Jan-Feb). Compared with their parent forcing, CPRCMs simulate more dry days but systematically higher daily rainfall rates, keeping the total rain biases low. During cloud band systems, the CPRCMs correctly reproduced the observed changes in tropical rain rates and their importance to climatology. Circulation analysis suggests that simulated lower subtropical rain rates during cloud bands systems, in contrast to the higher rates in the tropics, are associated with weaker northwesterly moisture flux from the Amazon towards southeast South America, more evident in the CPRCMs. Taken together, the results suggest that CPRCMs tend to be more effective at producing heavy daily rainfall rates than parametrised simulations for a given level of near-surface moist energy. The extent to which this improves or degrades biases present in the parent simulations is strongly region-dependent.