Moist heat impairs the human body’s ability to cool through sweat-based evaporative cooling, posing a serious health risk. In India, this risk is especially acute, since the Indian summer monsoon (ISM) brings abundant moisture, and socio–economic conditions significantly increase the exposure and vulnerability to moist heat. However, there is a limited understanding of the characteristics and large-scale drivers of moist heatwaves during the ISM. This study uses the ERA5 reanalysis to analyse moist heatwaves and their relationship with active and break periods of the ISM during 1940–2023. An empirical orthogonal function analysis of daily maximum wet-bulb temperature (Tw) anomalies reveals that the first two principal components (PCs) explain key patterns of variability of moist heatwaves, with PC1 controlling their occurrence and PC2 controlling their spatial extent. Whilst breaks in the monsoon favour moist heatwaves in eastern and peninsular India, active rainfall events, corresponding to phases 5–7 of the Boreal Summer Intraseasonal Oscillation, favour moist heatwaves in northern and northwestern India. Specific humidity plays a larger role than dry-bulb temperature in controlling Tw variability in India. The results of this study reveal important characteristics of moist heatwaves during the ISM and offer potential for developing forecasting tools, which could ultimately benefit stakeholders in India.

As the Earth continues to warm, humid heat extremes (HHEs) have emerged as a widely recognised threat to human health in equatorial Southeast Asia (SEA). While most studies have focused on climate trends, this study presents a comprehensive analysis of the synoptic and large-scale drivers of HHEs. On daily timescales, both the Madden-Julian Oscillation (MJO) and Kelvin waves are the leading modes of HHE variability. HHE risk increases by 1.2–1.4x during the dry-to-wet transition of the MJO phases, predominantly driven by increased near-surface specific humidity preceding the peak rainfall anomaly in phase 2 and increased shortwave radiation due to reduced cloud cover in phase 8. HHE risk increases by 1.3–2.0x during the dry phase of equatorial Kelvin waves, which drives subsidence and increased shortwave warming. On interannual timescales, El Niño is the leading driver, under which HHE risk increases 3 – 5x. Despite the limited overlap (19%) between wet- and dry-bulb temperature extremes, the differences in their temperature and humidity conditions, and their drivers, are small. This understanding lays the groundwork for short-range to seasonal forecasts, which are a crucial component of much-needed heat early warning systems.

Abstract Historical droughts and projected increase in drought risk have created an impetus for irrigation development across much of Sub-Saharan Africa, including Malawi. While irrigation can boost incomes and enhance food security, interactions between drought risk and irrigation planning are insufficiently explored, heightening the risks for expensive irrigation infrastructure and competition for water resources. We use a suite of bias-corrected outputs of 18 Global Climate Models coupled with a Soil Water Assessment Tool (SWAT) model to examine the dynamics of water availability and irrigation water demand (IWD) in five river basins over Malawi, southern Africa. We find that a strong coupling between precipitation and streamflow creates conditions suitable for quick propagation of drought signals typified by short lags between meteorological and hydrological droughts. Consequently, episodic spikes in IWD often coincide with periods of diminished streamflow thus reducing water supply for irrigation when IWD is most pronounced. Mean IWD is projected to increase across the five river basins (ranging from 20% to 50% for the low-mitigation scenario). The frequency of IWD spikes is also projected to increase. Meteorological-hydrological drought lags are projected to become shorter, increasing the prospect of IWD spikes coinciding with hydrological droughts more often. Having grounded the study on extremes, our results highlight other dimensions of climate risks for irrigation aside changing average climatic conditions. Such information is critical in supporting decisions around sustainable and resilient irrigation landscapes, but prevailing uncertainties demand robust decision-making approaches and model improvements.

Soil moisture is a key ingredient of humid heat through supplying moisture and modifying boundary layer properties. Soil moisture heterogeneity due to for example, antecedent rainfall, can strongly influence weather patterns; yet, its effect on humid heat is poorly understood. Idealized numerical simulations are performed with a cloud-resolving ( 500 m), coupled land-atmosphere model wherein wet patches on length-scales 25-150 km are prescribed. Compared to experiments with uniform soil moisture, humid heat is locally amplified by 1-4C, with maximum amplification for the critical soil moisture length-scale 50 km. Subsidence associated with a soil moisture-induced mesoscale circulation concentrates warm, humid air in a shallower boundary layer. The background wind and the magnitude of the wet-dry contrast control the relationship between and the humid heat amplification. Based on observed soil moisture patterns, these results will help to predict extreme humid heat on city and county scales across the Tropics.

The distribution of rainfall intensity through time at a fixed spatial location, referred to here as event temporal loading, can significantly influence hydrological and geomorphological responses, including run-off generation, urban flood risk, and soil erosion. Numerous approaches have been developed to analyse rainfall event temporal loading, but these differ in how they characterise rainfall behaviour and in the aspects of storm structure they emphasise. Emerging research further suggests that climate change may alter rainfall temporal loading in complex and regionally dependent ways, underlining the importance of clear and consistent approaches to its quantification. In this study, we identify 48 metrics which have been previously applied to describe event temporal loading, and define a further five metrics representing aspects not fully captured in existing metrics. We calculate these metrics for 233 128 rainfall events recorded at Danish rain gauges. We use data-driven cluster analysis to reveal how the metrics relate, highlighting groups of metrics that describe similar properties, and others that are more distinct. Based on this, we conceptualise five aspects of temporal loading: mass timing, peak timing, magnitude concentration, temporal concentration, and intermittency. We demonstrate that some metrics are robust to changes in rainfall event temporal resolution and pre-processing, while others are highly sensitive. Drawing on these findings, we recommend one representative metric per aspect: the 4th quartile mass fraction (or D50 if a continuous measure is preferred) for mass timing; peak position ratio for peak timing; the Gini coefficient for magnitude concentration; temporal standard deviation for temporal concentration; and the wet-dry transition rate for intermittency. Together, these recommendations provide a practical framework for deliberate metric selection and more consistent cross-study comparison of rainfall temporal loading.

Nowcasting developing convection is a crucial component of early warning systems in the tropics. While machine learning has proven effective for radar-based nowcasting, the lack of radar coverage across much of the tropics creates a significant capability gap. This study presents Simple Initiation and Intensification Nowcasting Neural Network (SII-NowNet), a machine learning tool that uses satellite brightness temperatures to produce probabilistic nowcasts of intensifying and initiating convection in the tropics. SII-NowNet is first demonstrated over Sumatra, Indonesia a densely populated tropical island with frequent convective activity. For nowcasts of intensifying convection, SII-NowNet outperforms an optical flow model for lead times of 1-6 h but begins to overpredict events beyond 3 h, indicating its limit of capability. For nowcasts of initiating convection, SII-NowNet's limit of capability is reached at 2 h, beyond which it overpredicts events and is outperformed by climatology. SII-NowNet is trained on 8661 samples (12 months of data), but sensitivity testing shows that the number of samples can be reduced to 3 weeks for intensification and 3 months for initiation, before it is outperformed by climatology. This has practical implications for the implementation and further development of SII-NowNet in resource-constrained settings. To exemplify generalizability in other tropical regions, SII-NowNet is tested over New Guinea, Zambia, Congo, and West Africa. Without retraining or region-specific tuning, SII-NowNet achieves skill scores comparable to those over Sumatra. Overall, SII-NowNet's promising results, combined with ease of applicability across the tropics, make it a valuable tool for future operational nowcasting.

Involving communities in flood early warning systems (FEWS) is increasingly recognised as an essential component of flood resilience. FEWS is considered to be integrated systems of flood forecasting and warnings, impact assessment, communication and preparedness which enable stakeholders to take appropriate actions to reduce the impacts of flooding. In the United Kingdom, voluntary, community-based flood groups can play an important role in local flood resilience, adding value to the work of Flood Risk Management Agencies including the Environment Agency, Local Authorities and Water Companies. However, little literature has examined how community-based flood groups use FEWS to help their local communities. In this paper we explore the use of FEWS by communities in the broadest sense, covering the use of any flood forecast or monitoring information and how this is used by flood groups to take action in the local community. We worked with 10 flood groups in England and found they used combinations of official and community-led information: (i) official information on flood warnings, weather forecasts, river-level observations and rain gauges and (ii) community-led bespoke warning systems at local hotspots including telemetry and video. Some of the Flood Groups were considerably advanced in how they analysed and presented this information, developing accessible dashboards and/or trigger points and alerts to support actions in the community. Five of the Flood Groups felt that their use of this information had recently prevented or reduced the impacts of flooding in their local community. However, the Flood Groups faced a range of challenges including technical and funding support for FEWS and wider governance challenges which should be addressed by State support. Support is particularly important in areas of significant flood risk and where community-led FEWS could complement and be integrated with state flood warnings. For example, where official flood warnings do not cover locations in sufficient detail or for key flood sources (e.g., surface water). In addition, the Flood Groups had mainly developed in affluent areas and appropriate interventions are also required in more disadvantaged communities. The study makes a strong case for State support for voluntary flood groups.

Extreme humid heat poses a serious risk to human health, reducing the body’s ability to cool itself through sweating. The impact on humans will increase under climate change, particularly in tropical regions, such as the Indian subcontinent, that are highly populated and already hot and humid. Whilst there is a growing body of research on dry-bulb temperature extremes, there is limited understanding of the meteorological drivers of humid heat extremes, particularly the role of moisture transport, rainfall, and evaporation of moisture from the Earth’s surface. In this study, we use ERA5 data to identify and analyse extreme humid heat events in the global tropics during 1993-2022. In particular, we focus on the relationship between rainfall and the occurrence of humid heat and use extremes in wet-bulb temperatures to define the humid heat events. We find that rainfall is a key driver of humid heat extremes across much of the global tropics. In monsoon regions, dry-bulb temperature extremes typically occur in the pre-monsoon period whereas wet-bulb extremes occur more frequently during the monsoon season. The role of rainfall varies between humid heat events characterised by extremes in dry-bulb temperature versus those characterised by extremes in humidity. In much of the global tropics, rainfall followed by a few days of dry clear weather primes the surface and boundary layer climates for the initiation of humid heat events. These events typically have extremes in dry-bulb temperatures accompanied by what we characterise as a sufficiently high level of humidity. In arid regions, away from irrigated areas, rainfall is critical for the initiation of humid heat and frequently occurs locally on the first day of humid heat events. These events typically have extremes in humidity whereas dry-bulb temperatures are less likely to be extreme. These findings are a step towards greater understanding of the meteorological drivers of humid heat extremes at the regional scale. They will be valuable in the evaluation of weather and climate models, will aid the use and interpretation of climate model projections, and ultimately inform the design of much needed early warning systems for humid heat extremes.

Previous work has explained the physical mechanisms behind nocturnal offshore propagation of convection southwest of Sumatra. Low‐level moisture flux convergence due to the land breeze front controls the progression of convection, typically a squall line, away from the coast overnight. However, the diurnal convection over the mountains occurs on only 57% of days in December–February (DJF) and propagates offshore on only 49% of those days. We investigate day‐to‐day variability in dynamical and thermodynamical conditions to explain the variability in diurnal convection and offshore propagation, using a convection‐permitting simulation run for 900 DJF days. A convolutional neural network is used to identify regimes of the diurnal cycle and offshore propagation behavior. The diurnal cycle and offshore propagation are most likely to occur ahead of an active Madden‐Julian oscillation, or during El Niño or positive Indian Ocean Dipole; however, any regime can occur in any phase of these large‐scale drivers, because the major control arises from the local scale. When the diurnal cycle of convection occurs over land, low‐level wind is generally onshore, providing convergence over the mountains, and low‐level humidity over the mountains is high enough to make the air column unstable for moist convection. When this convection propagates offshore, midlevel offshore winds provide a steering flow, combined with stronger convergence offshore due to more onshore environmental winds. Low‐level moisture around the coast also means that as the convection propagates, the storm‐relative inflow of air into the system adds greater instability than would be the case on other days.

Abstract. The distribution of rainfall over a storm's duration, known as the event temporal loading, can significantly influence hydrological and geomorphological responses, including run-off generation, urban flood risk, and soil erosion. A wide range of approaches have been developed to analyse rainfall event temporal loading, but these differ in how they characterise rainfall behaviour and in the aspects of storm structure they emphasise. Early research further suggests that climate change may alter rainfall temporal loading in complex and regionally dependent ways, underlining the importance of clear and consistent approaches to its quantification. In this study, we identify 52 metrics that have been applied to describe event temporal loading, and categorise them as classification metrics, summary statistics, or intermittency metrics. We calculate these metrics for 233,128 rainfall events recorded at Danish rain gauges, and demonstrate that, while some metrics are robust to changes in rainfall event temporal resolution and pre-processing, others are highly sensitive. Data-driven cluster analysis further reveals how various metrics relate to one another, highlighting groups of metrics that may be used interchangeably, and others that describe fundamentally different properties. Based on this, we conceptualise five aspects of temporal loading (mass timing, peak timing, magnitude concentration, temporal concentration, and intermittency) and recommend metrics to quantify each. Overall, the study provides a foundation for more deliberate and informed metric selection, helping to align research questions with appropriate representations of rainfall temporal loading, and offering a clearer basis for cross-study comparison.

Mesoscale ocean eddies contribute to the mixing and transport of water properties throughout the global ocean. Sea surface temperature anomalies associated with these eddies can influence atmospheric boundary layer stability, and thus the formation of clouds. The Maritime Continent experiences the modulation of convection and precipitation by processes operating over multiple spatial and temporal scales. However, mesoscale air-sea interactions, such as those associated with the eddies the region generates, remain understudied. Applying a sea surface height-based eddy detection and tracking algorithm, we show that lower latitude eddies, such as those in the Maritime Continent, are generally fewer in number, weaker, and shorter-lived, but larger and faster-propagating, compared to those at higher latitudes. Crucially, we highlight that eddies in the Maritime Continent can significantly modify air-sea heat exchange and the near-surface wind field. However, changes to column water vapor, cloud, and rainfall are less distinct. Compared to the Kuroshio Extension, a representative case study for the extratropics, atmospheric anomalies associated with eddies in the Maritime Continent are weaker, and decreasing in magnitude toward the lower latitudes. We hypothesize that weaker sea surface temperature anomalies associated with eddies in the Maritime Continent, coupled with their faster propagation and intraseasonal variability in convection over the region, reduce the likelihood and intensity of the instantaneous atmospheric imprint. This study therefore emphasizes the importance of the spatial and temporal scales with regard to air-sea interactions and their influence on cloud and rainfall across the Maritime Continent.

The risk posed globally by pluvial flooding to people and properties is growing due to urbanisation, infrastructure development and intensification of rainfall due to climate change. Whilst tools to model pluvial flood hazard have also advanced, there remains a knowledge gap around whether design storms used in modelling adequately represent the temporal distribution of rainfall within the extreme convective storms which drive flooding. In the UK, the industry standard design storm considers rainfall events to always have a singular, central intensity peak. Study of UK extreme rainfall observations suggests that loading of rainfall towards the start or end of events is in fact more common. This study highlights the sensitivity of pluvial flood extent, hazard and timing to the shape of the design rainfall profile for two urban catchments in northern England. We demonstrate that for events with the same accumulated rainfall depth, there is up to a 25% increase in total flood-affected area with a back-loaded compared to a front-loaded profile. Failing to account for the variability in event profile shapes observed in real events may result in substantial inaccuracies in the design of flood risk management solutions, leading to both underestimation and overestimation of the required measures.

Ambient humidity reduces the ability of the body to cool down through sweating, adding to the heat stress caused by elevated air temperature alone. Indeed, humid heat waves (HHWs) are already a threatfor humans, livestock and wildlife, and their impacts are projected to increase with global warming.HHWs result from the combination of thermodynamic and dynamic processes interacting on a range of time and space scales and whose relative importance may vary according to location and time of year.Africa is one continent where HHWs, defined here as extremes of wet-bulb temperature (Twb), are expected to become more important under global warming. Local-scale humid heat extremes may occur within more moderate larger-scale events across much of the continent. Yet, climatological characteristics of these smaller-scale events such as location and timing (in year and day) are poorly documented in the current climate, due to a lack of high-resolution data and research focus. Moreover, a comprehensive understanding of their meso- to synoptic-scale drivers is still lacking. Here, we explore these two issues using a 10-year pan-African convection-permitting model simulation that explicitly resolves land-atmosphere interactions, and particularly those involving moist processes that are instrumental to HHWs.We find humid heat extremes in semi-arid regions occurring in the core of the rainy season, on length scales down to a few tens of kilometers. During HHWs, Twb peaks several hours later than the climatological peak in the late morning. This diurnal cycle shift is likely due to HHWs typically developing in the aftermath of a rainfall event: the resulting positive anomaly in soil moisture induces increased latent heat fluxes, low level divergence, and a reduced PBL height, all ingredientsdisplaying sharp spatial gradients conducive to locally high Twb values. These results have implications for the improvement of localized HHW predictability based on local soil moisture conditions, a key step towards climate change adaptation through e.g., early-warning systems.

In a rapidly changing climate, increased resilience to surface water flooding (SWF) is urgently required. In the UK, responsible organisations are seeking to take bolder leadership in developing SWF forecasting and warning capabilities. A community effort is needed to identify where new data, technology and techniques offer opportunities to fill gaps in current capabilities and where the co-development of new science is required. This paper shares perspectives on priority areas for research and development from forecasters and responders, academics and consultants following a workshop held in Birmingham in January 2024.

Humid heatwaves are a growing risk to human and animal health, especially in tropical regions. While there is established research on dry-bulb temperature heatwaves, greater understanding of the meteorological drivers of extreme humid heat is urgently needed. In this study, we find that recent rainfall is a key control on the occurrence of humid heatwaves in the tropics and subtropics and its effect is regulated by the energy- or moisture-limited state of the land surface. In moisture-limited environments, heatwaves are likely during, or immediately after, enhanced rainfall. In energy-limited environments, heatwaves are likely after suppression of rainfall for two days or longer. The nature of the threat to health from heat stress varies by environment. It depends on local adaptation to temperature or humidity extremes, as well as vulnerability to absolute or anomalous extremes. Early warning systems, which reduce exposure and vulnerability to weather extremes, can benefit from this understanding of humid heatwave drivers, highlighting the possibility of predicting events using satellite-derived rainfall and surface moisture data.

Malaria transmission across sub-Saharan Africa is sensitive to rainfall and temperature. Whilst different malaria modelling techniques and climate simulations have been used to predict malaria transmission risk, most of these studies use coarse-resolution climate models. In these models convection, atmospheric vertical motion driven by instability gradients and responsible for heavy rainfall, is parameterised. Over the past decade enhanced computational capabilities have enabled the simulation of high-resolution continental-scale climates with an explicit representation of convection. In this study we use two malaria models, the Liverpool Malaria Model (LMM) and Vector-Borne Disease Community Model of the International Centre for Theoretical Physics (VECTRI), to investigate the effect of explicitly representing convection on simulated malaria transmission. The concluded impact of explicitly representing convection on simulated malaria transmission depends on the chosen malaria model and local climatic conditions. For instance, in the East African highlands, cooler temperatures when explicitly representing convection decreases LMM-predicted malaria transmission risk by approximately 55%, but has a negligible effect in VECTRI simulations. Even though explicitly representing convection improves rainfall characteristics, concluding that explicit convection improves simulated malaria transmission depends on the chosen metric and malaria model. For example, whilst we conclude improvements of 45% and 23% in root mean squared differences of the annual-mean reproduction number and entomological inoculation rate for VECTRI and the LMM respectively, bias-correcting mean climate conditions minimises these improvements. The projected impact of anthropogenic climate change on malaria incidence is also sensitive to the chosen malaria model and representation of convection. The LMM is relatively insensitive to future changes in precipitation intensity, whilst VECTRI predicts increased risk across the Sahel due to enhanced rainfall. We postulate that VECTRI’s enhanced sensitivity to precipitation changes compared to the LMM is due to the inclusion of surface hydrology. Future research should continue assessing the effect of high-resolution climate modelling in impact-based forecasting.

Surface water flooding (SWF) is a severe hazard associated with extreme convective rainfall, whose spatial and temporal sparsity belie the significant impacts it has on populations and infrastructure. Forecasting the intense convective rainfall that causes most SWF on the temporal and spatial scales required for effective flood forecasting remains extremely challenging. National-scale flood forecasts are currently issued for the UK and are well regarded amongst flood responders, but there is a need for complementary enhanced regional information. Here we present a novel SWF-forecasting method, FOREWARNS (Flood fOREcasts for Surface WAter at a RegioNal Scale), that aims to fill this gap in forecast provision. FOREWARNS compares reasonable worst-case rainfall from a neighbourhood-processed, convection-permitting ensemble forecast system against pre-simulated flood scenarios, issuing a categorical forecast of SWF severity. We report findings from a workshop structured around three historical flood events in Northern England, in which forecast users indicated they found the forecasts helpful and would use FOREWARNS to complement national guidance for action planning in advance of anticipated events. We also present results from objective verification of forecasts for 82 recorded flood events in Northern England from 2013–2022, as well as 725 daily forecasts spanning 2019–2022, using a combination of flood records and precipitation proxies. We demonstrate that FOREWARNS offers good skill in forecasting SWF risk, with high spatial hit rates and low temporal false alarm rates, confirming that user confidence is justified and that FOREWARNS would be suitable for meeting the user requirements of an enhanced operational forecast.

Patterns in extreme precipitation across the Maritime Continent in southeast Asia are known to be modulated by many processes, from large-scale modes of variability such as the Madden-Julian oscillation, to finer-scale mechanisms such as the diurnal cycle. Transient mid-level dry air intrusions are an example of a feature not extensively studied over the Maritime Continent, which has the potential to influence rainfall patterns. Here, we show that these dry air intrusions originate from upper level disturbances along the subtropical jet. Mid-level cyclonic circulation anomalies northwest of Australia from December to February (DJF) intensify westerlies in the southern Maritime Continent, advecting dry air eastward. In contrast, mid-level anticyclonic circulation anomalies northwest of Australia from June to August (JJA) intensify southern Maritime Continent easterlies, advecting dry air westward. The resultant transport direction of associated air parcels is also dependent on the seasonal low-level monsoon circulation. Dry air intrusions are important in influencing low-level wind and rainfall patterns, suppressing rainfall over seas near the southern Maritime Continent in both seasons, as well as over southern Maritime Continent islands in DJF and the Indian Ocean in JJA. In both seasons there is enhanced rainfall to the east of the intrusion, where there is moist return flow to the extratropics. This study highlights the importance of synoptic-scale extratropical features in influencing meteorological patterns in the Tropics. Many processes influence rainfall patterns observed across the Maritime Continent in southeast Asia. Dry air intrusions (a tropical-extratropical interaction) arriving in the southern Maritime Continent are due to upper level disturbances along the subtropical jet. This precursor mechanism and resultant intrusion trajectories show differences across seasons. However, dry air intrusions predominantly suppress rainfall, regardless of the time of year, and can modify low-level moist flow and lead to enhanced rainfall anomalies to the east.image

A multi-season convection-permitting regional climate simulation of the Maritime Continent (MC) using the Met Office Unified Model (MetUM) with 2.2 km grid spacing is presented and evaluated. The simulations pioneer the use of atmosphere–ocean coupling with the multi-column K profile parametrisation (KPP) mixed-layer ocean model in atmospheric convection-permitting climate simulations. Comparisons are made against a convection-parametrised simulation in which it is nested and which in turn derives boundary conditions from the ERA5 reanalysis. This paper describes the configuration, performance of the mean state and variability in the two simulations compared against observational datasets. The models have both minor sea surface temperature (SST) and wet precipitation biases. The diurnal cycle, representation of equatorial waves, and relationship between SST and precipitation are all improved in the convection-permitting model compared to the convection-parametrised model. The Madden–Julian oscillation (MJO) is present in both models with a faster-than-observed propagation speed. However, it is unclear whether fidelity of the MJO simulation is inherent to the model or whether it predominantly arises from the forcing at the boundaries.

The Maritime Continent (MC) regularly experiences powerful convective storms that produce intense rainfall, flooding and landslides, which numerical weather prediction models struggle to forecast. Nowcasting uses observations to make more accurate predictions of convective activity over short timescales (∼ 0–6 h). Optical flow algorithms are effective nowcasting methods as they are able to accurately track clouds across observed image series and predict forward trajectories. Optical flow is generally applied to weather radar observations; however, the radar coverage network over the MC is not complete and the signal cannot penetrate the high mountainous regions. In this research, we apply optical flow algorithms from the pySTEPS nowcasting library to satellite imagery to generate both deterministic and probabilistic nowcasts over the MC. The deterministic algorithm shows skill up to 4 h on spatial scales of 10 km and coarser and outperforms a persistence nowcast for all lead times. Lowest skill is observed over the mountainous regions during the early afternoon, and highest skill is seen during the night over the sea. A key feature of the probabilistic algorithm is its attempt to reduce uncertainty in the lifetime of small-scale convection. Composite analysis of 3 h lead time nowcasts, initialised in the morning and afternoon, produces reliable ensembles but with an under-dispersive distribution and produces area under the curve scores (i.e. ratio of hit rate to false alarm rate across all probability thresholds) of 0.80 and 0.71 over the sea and land, respectively. When directly comparing the two approaches, the probabilistic nowcast shows greater skill at ≤ 60 km spatial scales, whereas the deterministic nowcast shows greater skill at larger spatial scales ∼ 200 km. Overall, the results show promise for the use of pySTEPS and satellite retrievals as an operational nowcasting tool over the MC.