RCMs, when constrained at their lateral boundaries by an ESM, dynamically downscale atmospheric conditions to finer resolutions while explicitly resolving physical processes that are more detailed or regionally relevant than those represented in models run at the global scale. Such an approach is particularly suited for the Antarctic ice sheet, where SMB is strongly influenced by localized processes such as orographic precipitation, surface melting, or katabatic winds. In this study, we adopt this framework to evaluate the Antarctic SMB by dynamically downscaling an ESM with three RCMs: MAR, RACMO, and HIRHAM. In each RCM, boundary conditions are imposed every six hours at all atmospheric levels (pressure, zonal and meridional wind components, temperature, specific humidity), and at the ocean surface (sea-ice concentration and sea-surface temperature). In addition, MAR and RACMO apply an internal nudging towards driving wind and temperature fields in the upper atmosphere to ensure consistency with the large-scale circulation over the interior model domain. The models analyzed in this study are not coupled to an ice-dynamics model. They therefore assume a fixed ice-sheet geometry (topography and extent), which excludes both melt–elevation feedback and the effects of topographic changes on atmospheric circulation .

This subsection provides a concise description of each of the four models used in this study, along with a comparative summary of their snowpack schemes, which govern the representation of key SMB components such as melt, refreezing, and the resulting runoff. It is also important to note that only RACMO, among the model versions employed here, includes snow transport by wind in the computation of SMB.

The omission of wind-driven erosion and deposition is not expected to compromise our intercomparison since runoff rapidly becomes the dominant component of surface ablation in Antarctica (by at least one order of magnitude compared to other ablation terms; , 2021) in a warmer climate, and because wind-driven snow erosion is supposed to interact only locally with the other SMB components . In particular, under warmer conditions, the increased occurrence of surface liquid water further limits blowing snow . From here on, this difference will not be repeatedly mentioned, so as to focus solely on processes related to surface and snowpack water content (melt, rainfall, percolation, retention, refreezing, and runoff). The main model features relevant to capturing these processes are listed in Table . The specific model versions are detailed in each paragraph.

When comparing projections from the different models, it is crucial to consider the specific parameterizations implemented in each RCM (Table ). One key difference lies in how atmosphere–surface interactions are handled: HIRHAM operates with a decoupled atmosphere–surface setup, whereas MAR, RACMO, and CESM2 employ fully interactive coupling, allowing feedbacks from the surface to influence the atmosphere. The models also differ in the initialization of their snow and firn schemes, and all use a different spin-up method. Total snowpack thickness ranges from 20 m in MAR to 62 mw.e. in HIRHAM, when it is variable in RACMO and set to 10 mw.e. in CESM2, while the vertical layering varies in both number and thickness of layers in the four models. Percolation – the downward movement of liquid water within the snowpack – is represented in MAR, RACMO, and CESM2 using a bucket-type approach: once a layer reaches its maximum water retention capacity (also known as irreducible water saturation), excess water is passed to the layer below, and any remaining water that is not retained in the firn or refrozen within the current time step contributes to runoff. In all four models, runoff is treated as a purely vertical process and does not account for lateral water fluxes. HIRHAM follows a more physical approach in which the vertical flow of liquid water is governed by a Darcy's law . In any of these strategies, the efficiency of the percolation process depends on the water retention capacity, defined as a percentage of the available pore space of each layer, which differs between models from a fixed 2 % in RACMO to a maximum set to 7 % per layer in HIRHAM . Albedo specifications for fresh snow and bare ice also vary across models, directly affecting the strength of the melt–albedo feedback. Lower albedo values, for instance, enhance melt through increased absorption of solar radiation. These parametric and structural differences are important sources of variability in ice sheet surface mass balance projections, as highlighted in previous comparative studies over Antarctica and Greenland .

For the purpose of inter-model comparison, all outputs from the four models were bi-linearly interpolated onto a common 10 km regular stereographic polar grid (EPSG3031), which differs from and is finer than the native grids of each model. This is especially significant for CESM2, whose native grid (100 km) is an order of magnitude coarser than the common grid, so that the interpolation adds no real small-scale information. This grid has been adopted from the Ice-sheet Model Intercomparison Project phase 6 (ISMIP6) . A common ice mask, defined as the maximum ice extent common to the four models, was also applied to each model to ensure comparisons over identical areas, as previous studies demonstrate that considering different ice masks can have a major impact on SMB results . The digital elevation model used to generate model topography on each model's native grid differs among the four models (Sect. ). This leads to differences in surface elevation between models. While RCMs generally show good agreement with differences most pronounced in areas of steep topographic gradients, CESM2 generally features higher elevations over ice shelves and lower elevations over grounded ice (Fig. S1 in the Supplement). The 1995–2014 period was chosen as the 20-year contemporary reference, during which Antarctic SMB remained relatively stable , in line with ISMIP6 recommendations .

In order to compare the four models on an equal footing, the analysis is primarily conducted in terms of anomalies with respect to their own present day climate. We refer to “future anomalies” as the difference between the mean of the 2080–2099 period and that of the reference period 1995–2014, and into “time series anomalies” as the deviation of each annual value from this same reference mean. This approach highlights projected trajectories while minimizing the influence of systematic biases that may arise from parametric and structural differences between the models and the resulting representation of the contemporary climate. Furthermore, by defining the reference period as representative of a stable climate , each model can establish its own equilibrium state, particularly with respect to the surface energy balance. However, differences in this initial equilibrium affect both the magnitude and direction of projected long-term anomalies. Therefore, to identify the mechanisms underlying inter-model discrepancies, this anomaly-based analysis is complemented by an evaluation in absolute values.

Both the anomalies and absolute values discussed above are derived from annual mean or cumulative quantities rather than seasonal (e.g. austral summer, DJF) values, even though the processes discussed here (melt, runoff, melt-albedo feedback) are primarily active during the summer months. This choice is dictated by the structure of the available model output: the projections were produced and archived on an annual calendar basis through 2100, and monthly or seasonal components are not available for this analysis. As a result, all quantities presented throughout this study are restricted to annual means and cumulative values, a limitation that should be kept in mind when interpreting the inter-model comparisons.

In addition to the future projections discussed above, present-day model performance is also evaluated using historical simulations forced by ERA5 reanalysis over the 1980–2022 period, for which daily output is available. This higher temporal resolution allows for a direct comparison against daily satellite-derived melt products, namely AMSR2 and SSM/I , which are used in Sect.  to evaluate melt extent and duration across models. Following , model and satellite-derived melt fields are compared using three metrics: the Cumulative Melting Surface (CMS; dkm2), defined as the spatial yearly (calendar year) sum of melt days multiplied by the pixel area (10km×10km); the Maximum Melting Surface (MMS; km2), corresponding to the surface over which melting is detected at least once during the corresponding year; and the Mean Melt Duration (MMD; d), obtained by dividing the CMS by the MMS.

Figure  shows SMB future anomalies over the entire Antarctic Ice Sheet for the period 2080–2099 relative to 1995–2014, expressed in mmw.e.yr-1 (Fig. a–d). All four models, HIRHAM (Fig. a), MAR (Fig. b), RACMO (Fig. c), and CESM2 (Fig. d), show a clear contrast between the grounded ice sheet, characterized by increased SMB (positive anomalies), and the floating ice shelves, which mostly experience a decline in SMB (negative anomalies). The locations mentioned hereafter are illustrated Fig. S2. In HIRHAM, all ice shelves exhibit negative anomalies. MAR simulates a more localized decrease, notably over the Amery ice shelf, along the ice shelves of Queen Maud Land and over Larsen ice shelf. RACMO shows relatively stable SMB across most ice shelves, close to present-day equilibrium, except over Larsen ice shelf and a few shelves in Queen Maud Land, where negative anomalies also appear. In CESM2, negative anomalies are primarily observed over the Antarctic Peninsula and along the ice shelves of Queen Maud Land. In MAR, RACMO, and CESM2, the large Ronne–Filchner and Ross ice shelves maintain generally positive anomalies. In addition, MAR shows negative SMB values along parts of the grounding lines of the Ross and Ronne–Filchner ice shelves. These localized deficits are very likely related to interactions between the local atmospheric circulation and the Transantarctic Mountains, including foehn-induced or adiabatic warming, as well as enhanced snowfall sublimation . Over the grounded ice sheet, all four models consistently indicate a general increase in SMB, with similar spatial patterns: stability over the Antarctic plateau and a pronounced SMB increase in the western part of the ice sheet.

Future SMB means for the period 2080–2099 (Fig. d–f) confirm these trends, with enhanced ablation in HIRHAM, where most ice shelves exhibit negative SMB. In MAR and RACMO, negative values are primarily confined to the Antarctic Peninsula and the Amery Ice Shelf. In CESM2, only the tip of the Antarctic Peninsula shows negative SMB. Moreover, relative to the RCMs, CESM2 reproduces a relatively latitudinally homogeneous field with high values, dominated by a coast-to-plateau gradient. This pattern directly reflects the limited horizontal resolution, which controls the representation of topographic gradients and influences orographic precipitation in the model, particularly visible along the margins of Queen Maud Land, in West Antarctica, and around the Amery Ice Shelf. Given this pronounced distinction between grounded and floating regions, and to avoid obscuring their divergent SMB trajectories, we choose to analyze them separately for the remainder of the study.

Under warmer conditions, the continent-scale SMB is primarily governed by precipitation (the main accumulation component at the surface) and runoff (the main ablation component); sublimation, although locally significant, remains relatively minor at the continental scale . Figure  shows the temporal evolution of SMB, precipitation, and runoff anomalies integrated over the grounded ice and the ice shelves. These time series highlight inter-model consistency over the grounded ice, but pronounced differences on the ice shelves by 2100.

Over the grounded ice sheet, the models consistently project an increase in SMB by the end of the century (Fig. a), with future anomalies for 2080–2099 ranging from +610±69 Gtyr-1 for HIRHAM to +870±93 Gtyr-1 for RACMO (Table ). Despite differences in runoff (Fig. e), grounded SMB is largely dominated by precipitation, with anomalies ranging from +974±111 Gtyr-1 for MAR to +1171±122 Gtyr-1 for HIRHAM (Table ). In contrast, projections for the ice shelves reveal substantial divergence (Fig. b). RACMO simulates a nearly stable SMB, close to present-day equilibrium, with an anomaly of +25±19 Gtyr-1, resulting from a near balance between runoff (+170±37 Gtyr-1) and precipitation (+203±23 Gtyr-1, Table ). Conversely, HIRHAM, MAR and CESM2 indicate net surface mass loss, with anomalies of -529±72, -209±58, and -118±36 Gtyr-1, respectively. These losses result from increased runoff that is no longer compensated by precipitation: runoff anomalies reach +775±160 Gtyr-1 for HIRHAM, +390±76 Gtyr-1 for MAR, and +329±59 Gtyr-1 for CESM2. Such intermodel spread is striking at the end of the century, with HIRHAM exhibiting the highest runoff rates of about 4.6 times those of RACMO, which is the model that has the most constant SMB.

These contrasts reflect not only differences in runoff magnitude but also in the timing of negative anomalies emergence over ice shelves: HIRHAM anticipates a transition to negative SMB anomalies as early as 2040, whereas it appears only around 2060 for MAR and 2070 for CESM2. Thus, inter-model dispersion over the ice shelves largely stems from the representation of runoff, in both its intensity and temporal evolution. To reduce dependence on emission scenario timing, the following analysis considers SMB component evolution as a function of the climate response (near-surface air temperature) rather than absolute time.

Under warmer conditions, several mechanisms can enhance both the frequency and magnitude of surface runoff over the ice sheet: increased rainfall, greater liquid-water production driven by cloud-emitted longwave radiation e.g. and by the melt–albedo feedback, and a diminished water-retention capacity of the firn. Figure  presents annual (calendar year) near-surface air temperatures for the four models, highlighting persistent differences of 2–3 °C over both grounded ice and ice shelves. Given the strong temperature dependence of surface melt and the inter-model differences in near-surface temperature, the following results are discussed in terms of absolute near-surface temperature (note that these discrepancies are only negligibly affected by differences in topography; see Fig. S3). This approach allows us to account for the structural differences between models and to highlight their present-day states, rather than focusing solely on their temporal evolution. Moreover, to make the discussion clearer, and since the differences are driven by distinct physical mechanisms, we focus first on HIRHAM's evolution, before considering how MAR, RACMO, and CESM2 diverge from one another.

The melt–albedo feedback is known to be a key driver of melt rate evolution and a strongly nonlinear process e.g.. Albedo directly controls the net shortwave radiation (SWN); hence, SWN anomalies are used here to represent albedo changes. Focusing first on the ice shelves, HIRHAM projects the strongest increase in SWN by the end of the simulation +6.76±0.41 Wm-2, compared to +0.32±0.11 Wm-2 for MAR, +0.68±0.22 Wm-2 for RACMO, and +1.27±0.18 Wm-2 for CESM2 (Fig. b). This pronounced anomaly in HIRHAM clearly indicates that the melt–albedo feedback strongly amplifies simulated melt rates.

The runaway of this positive feedback can be linked to HIRHAM's already elevated contemporary melt and refreezing. HIRHAM melt and refreezing exceeds by factors of 5 and 4 from those of MAR and RACMO, and by factors of 3 and 2 from those of CESM2 over the ice shelves, based on mean values for the period 1995–2014 (Table S1 in the Supplement). Moreover, because HIRHAM starts from slightly lower temperatures (252 K), when we compare its melt and refreezing rates with those at the starting temperatures of RACMO (254 K), MAR and CESM2 (255 K), its rates already match those projected at the end of the simulation under a high-emission scenario (Fig. b and d). The combination of early high melt rates and distinct SWN evolution results in considerably higher projected melt for HIRHAM, mainly driven by the melt–albedo feedback. As shown in Fig. , HIRHAM's melt, refreezing and runoff (panels a-d and g-h) stand out throughout the simulation. The projected melt anomalies over the ice shelves reach +903±90 Gtyr-1 for HIRHAM, compared to +584±84 Gtyr-1 for MAR, +413±57 Gtyr-1 for RACMO, and +675±86 Gtyr-1 for CESM2 (Table ). As a result of the high refreezing rates simulated by HIRHAM over the ice shelves, the firn progressively loses retention and cold capacity (Fig. d). The curve shows an inflection point around 259–260 K, indicating the temperature at which the firn reaches its maximum refreezing capacity. Beyond this threshold, successive melt–refreeze cycles further reduce firn porosity, limiting water storage and refreezing, increasing the fraction of meltwater that is evacuated as runoff (Fig. h). Over the grounded ice, HIRHAM also produces higher melt and refreezing rates from the start of the simulation (by factors of 6 and 4.5 relative to MAR and RACMO, Table S1). This results in a strong melt trend; however, the firn's maximum refreezing capacity has not yet been reached. The resulting runoff over the grounded ice is nevertheless significantly higher compared to the other models, but it remains small relative to the local solid precipitation, as illustrated in Fig. . However, this SWN runaway behaviour and the high melt and refreezing rates at the start of the simulation cannot be explained by differences in the baseline surface energy balance, surface albedo or downwelling longwave radiation (Fig. S4). Nevertheless, despite relative agreement on the contemporary SMB, these findings raise serious questions about the plausibility of HIRHAM's simulated melt and refreezing rates under current climate conditions.

MAR and RACMO are closely aligned in both their melt and refreezing rates over the reference period (Table S1) and in their evolution with temperature (Fig. ). Because melt rates are comparable at a given temperature, the near surface temperature offset leads MAR to reach stronger melt than RACMO at the end of the simulation, with projected temperatures of 263.2±0.5 and 261.9±0.4 K, respectively, corresponding to differences of 171 Gtyr-1 over the ice shelves and 150 Gtyr-1 over the grounded ice. Refreezing in the two models evolves in parallel, but is shifted in temperature (2 °C over grounded ice and 1.3 °C over the shelves). This shift translates into differences in refreezing for the same melt rate and therefore contribute to the divergence in runoff between MAR and RACMO (Fig. h). This divergence is further amplified by structural differences in the firn representation: the higher retention capacity prescribed in MAR (5 % vs. 2 % in RACMO) partly compensates for its thinner firn, while the initially warmer MAR snowpack likely limits refreezing efficiency regardless of firn air content. In addition, lower snowfall accumulation over the ice shelves in MAR reduces firn renewal, and enhanced rainfall associated with warmer conditions further promotes runoff.

For CESM2, the projected melt evolution over the ice shelves closely follows that of MAR and RACMO. Its similarity to MAR in terms of near surface temperature (Fig. ) translates into comparable future melt anomalies, with a difference of only 91 Gtyr-1, which is on the same order as their interannual variability 84 Gtyr-1 for MAR and 86 Gtyr-1 for CESM2. Over the grounded ice, CESM2 projects a stronger melt increase, akin to HIRHAM. However, CESM2 predicts persistent and substantial refreezing that continues to grow without reaching a plateau, thereby limiting the acceleration of runoff. In addition, CESM2 exhibits an integrated runoff component evolution over the entire ice shelves that is remarkably similar to MAR and RACMO, despite its native resolution of 110 km.

Although liquid precipitation represents only a small fraction of the total liquid water (melt + rainfall) at the surface, accounting for 8 % in HIRHAM, 10 % in MAR, 7 % in RACMO, and 17 % in CESM2 in future anomalies (Table ), the models exhibit divergent rainfall rates for similar near-surface temperatures, with HIRHAM and CESM2 producing substantially higher rates than MAR and RACMO for a given temperature (Fig. ). However, HIRHAM shows an evolution comparable to MAR, reaching similar mean (2080–2099) liquid precipitation rates of 78±15 and 66±12 Gtyr-1 over the ice shelves, and 83±17 and 77±16 Gtyr-1 over grounded ice, respectively. This indicates that liquid precipitation does not contribute to the runoff divergence between HIRHAM and MAR. As for MAR and RACMO, they again display a very similar rainfall–temperature relationship (Fig. e and f). Nevertheless, the temperature offset between MAR and RACMO results in higher rainfall rates in MAR by the end of the simulation compared to RACMO (27±7 Gtyr-1 over grounded ice and 33±7 Gtyr-1 over the ice shelves), contributing to their runoff differences. The high liquid precipitation rates reached by CESM2 (171±20 and 160±17 Gtyr-1) explain its similarity in runoff compared to MAR, despite much greater refreezing rates in CESM2 (Fig. c and d) and very similar melt rates (Fig. a and b). This high estimated liquid precipitation in CESM2 has already been reported by , who identified a systematic rainfall bias in the model. While rainfall remains a relatively small contributor overall compared to surface meltwater production , it nonetheless affects the surface hydrological regime and hence runoff by adding liquid water to the surface.

This surface hydrological regime can be described by the ratio of surface liquid water (melt plus rainfall) to net accumulation (snowfall minus sublimation), defined in the literature as the Melt Over Accumulation (MOA) ratio . Theoretical considerations suggest that, when MOA exceeds 0.7, that is, when liquid water input at the surface reaches 70 % of net accumulation, the snowpack can no longer retain and renew pore space, triggering runoff and/or melt-pond formation, thereby increasing the risk of ice-shelf disintegration via hydrofracturing.

Figure  shows the integrated surface liquid water and net accumulation over grounded ice (left panel) and ice shelves (right panel). Two distinct regimes emerge over ice shelves: HIRHAM undergoes a much earlier transition than the other models. While its simulated accumulation remains comparable to the other RCMs, excess rainfall and melt push HIRHAM beyond the threshold as early as 253.4±0.5 K, several degrees earlier than MAR, RACMO, and CESM2 (259.9±0.4, 260.7±0.6, and 259.2±0.4 K, respectively) represented by the vertical dotted lines in Fig.  (integrated MOA in Fig. S5). This is particularly striking, as HIRHAM's mean temperature during the reference period (1995–2014), is 253.2±0.7 K. This suggests an unrealistically early onset of runaway runoff in HIRHAM, unlike in the other models, where melt and accumulation remain more balanced, making such a scenario far less likely. We also note that over ice shelves, MAR simulates lower accumulation than RACMO (by about 86 Gtyr-1). For comparable melt rates, this reduced accumulation causes MAR to reach the threshold at lower temperatures than RACMO, thereby promoting runoff. In contrast, this threshold is not reached over the integrated grounded ice, despite the large accumulation.

The spatial distribution of the temperature anomalies at which the MOA threshold of 0.7 is reached further supports this interpretation (Fig. ). HIRHAM stands out by reaching the threshold at near-zero or even negative temperature anomalies, indicating that this critical state exposing ice shelves to a risk of destabilization already occurs during or even before the reference period across most ice shelves (Fig. S6). In contrast, MAR, RACMO, and CESM2 show broadly consistent threshold anomalies, with only localized differences – particularly over the Filchner–Ronne and Ross ice shelves-likely reflecting spatial variations in accumulation and melt intensity among the models. Despite these local discrepancies, a coherent pattern emerges over key regions such as the Amery and Larsen ice shelves, where the threshold corresponds closely to the mean temperature of the reference period. This agreement is consistent with observations from the Amery Ice Shelf which experiences intense surface meltwater ponding , as well as the Larsen sector where successive collapses of ice shelves – interpreted as being linked to enhanced surface melt – were documented between 1995–2020 e.g..