arxiv: 2604.25447 · v1 · submitted 2026-04-28 · ⚛️ physics.ao-ph

Recognition: unknown

Evaluating local climate in global storm-resolving models with the K\"oppen-Geiger classification

Cathy Hohenegger, Chiel C. van Heerwaarden, Edgar Dolores-Tesillos, Emanuel Dutra, Erich Fischer, Imme Benedict, Jonathan D. Willie, Junhong Lee, Lukas Brunner, Menno A. Veerman, Olivia Martius, Sarah N. Warnau, Ulrike Proske, Xabier Pedruzo-Bagazgoitia

Pith reviewed 2026-05-07 14:12 UTC · model grok-4.3

classification ⚛️ physics.ao-ph

keywords storm-resolving modelsKöppen-Geiger classificationclimate zonesICONIFS-FESOMregional biasesprecipitation errorsclimate projections

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The pith

Storm-resolving models reproduce global climate zone patterns but show regional biases driven mainly by precipitation errors.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper tests two global storm-resolving models against the Köppen-Geiger climate classification to check whether they deliver accurate local-scale climate data at 9 km resolution. Both models correctly place the five broad climate categories across the globe in 30-year simulations, which is promising for these relatively new high-resolution approaches. Yet they underrepresent tropical rainforests in the Amazon and Africa because of too little rain in the dry months, and one model rains so much over Australia that it nearly erases the desert zone there. Precipitation mistakes cause most zone misclassifications, with temperature errors mattering only near certain mid-latitude boundaries. Readers should care because these models are meant to act as digital twins of Earth for local climate impacts and adaptation planning, so spotting these gaps helps focus future improvements.

Core claim

Both ICON and IFS-FESOM capture the global distribution of the five main Köppen-Geiger climate categories in 30-year simulations at 9 km resolution under the SSP3-7.0 scenario. Substantial regional biases remain, including underestimation of tropical rainforest extent due to insufficient dry-month precipitation in Amazonia and equatorial Africa, near-elimination of hot arid desert in Australia by ICON through excess precipitation, and opposing biases along the temperate-continental boundary. Precipitation errors dominate misclassifications while temperature biases play a secondary role at mid-latitude zone edges. The two models agree with CMIP6 projections on the direction of future climateゾ

What carries the argument

The Köppen-Geiger classification system, which assigns climate zones such as tropical rainforest (Af) or hot desert (BWh) using monthly temperature and precipitation thresholds, applied to diagnose accuracy of local climate information in storm-resolving model output.

If this is right

The two models and CMIP6 projections agree on the direction of climate zone shifts under warming, including expansion of tropical savanna and hot desert at the expense of subarctic, tundra, and ice cap zones.
Inter-model differences in present-day climate exceed the 30-year climate change signal for many zones, calling for caution in regional projections.
Precipitation errors are the primary driver of climate zone misclassifications, while temperature biases are secondary and limited to mid-latitude boundaries.
Substituting observed fields shows that ICON produces excessive precipitation over Australia while IFS-FESOM matches the desert zone there more closely.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

Routine use of Köppen-Geiger classification could provide a simple, standardized benchmark to monitor improvements as storm-resolving models increase resolution or refine their physics.
The dominance of precipitation biases suggests targeted development on convective and land-surface processes in tropical and arid regions would yield the largest gains in classification accuracy.
Because present-day model spread exceeds the climate change signal, multi-model ensembles may be required before relying on these simulations for specific regional adaptation decisions.

Load-bearing premise

The Köppen-Geiger thresholds based solely on monthly temperature and precipitation are sufficient and unbiased for judging whether the models deliver accurate local-scale climate information.

What would settle it

Comparison of model output against high-resolution observational temperature and precipitation records in the Amazon, Australia, and western Europe to check whether the models' assigned climate zones match the observed classifications in regions of disagreement.

Figures

Figures reproduced from arXiv: 2604.25447 by Cathy Hohenegger, Chiel C. van Heerwaarden, Edgar Dolores-Tesillos, Emanuel Dutra, Erich Fischer, Imme Benedict, Jonathan D. Willie, Junhong Lee, Lukas Brunner, Menno A. Veerman, Olivia Martius, Sarah N. Warnau, Ulrike Proske, Xabier Pedruzo-Bagazgoitia.

**Figure 1.** Figure 1: K¨oppen-Geiger climate classification from observations and storm-resolving models. Dominant climate zone for a) the observational reference of Beck et al. (2018), b) ICON, and c) IFS-FESOM, for the period 2021–2025. Major climate zones are tropical (A), arid (B), temperate (C), continental (D), and polar (E), see Supplement A for a full overview. Markers indicate the eight locations examined in Sec. 3.2. 5 view at source ↗

**Figure 2.** Figure 2: Monthly climatology of temperature and precipitation at eight locations with classification discrepancies. Annual cycle of monthly mean 2-metre temperature (lines, left axis) and precipitation (bars, right axis) for observations (gray), ICON (blue), and IFS-FESOM (red) over the period 2021–2025. Locations are indicated in Fig. 1a, and are ordered from west to east. In West Africa, the Sahelian transition… view at source ↗

**Figure 3.** Figure 3: Attribution of climate classification errors to temperature and precipitation biases. The K¨oppen-Geiger classification is recomputed for a) ICON and b) IFS-FESOM after substituting either the model’s temperature or precipitation with the corresponding observed field from Beck et al. (2018). Grid points are coloured according to whether the correct classification is restored by replacing temperature (green… view at source ↗

**Figure 4.** Figure 4: Present-day and future land cover fractions by K¨oppen-Geiger climate zones. Percentage of global land area occupied by each climate zone for the observational reference (no hatch); Beck et al. 2018 for present, CMIP6-based projections from Beck et al. 2023 for future), ICON (dotted) and IFS-FESOM (diagonal stripes). a) present-day period (2021–2025). b) future period (2045–2049) under the SSP3-7.0 scenari… view at source ↗

read the original abstract

Global storm-resolving models aspire to become digital twins of the Earth, delivering information at the local scale at which humans experience climate. We evaluated how well two such models, ICON and IFS-FESOM, reproduce the climate as classified by the K\"oppen-Geiger system, using 30-year (2020-2049) simulations from the nextGEMS project at 9~km global resolution under SSP3-7.0 scenario. Both models capture the global distribution of the five main climate categories, encouraging given the infancy of storm-resolving climate modelling. Substantial regional biases nonetheless remain. Both underestimate tropical rainforest (Af) extent due to insufficient dry-month precipitation in Amazonia and equatorial Africa. ICON almost eliminates hot arid desert (BWh) across Australia through excessive precipitation, while IFS-FESOM reproduces it well. The two models show opposing biases along the temperate--continental boundary: IFS-FESOM winters are too cold in western Europe, ICON winters too warm. Substituting observed temperature or precipitation into the model fields reveals that precipitation errors dominate misclassification, while temperature biases play a secondary role confined to mid-latitude climate zone boundaries. Under climate change, the two models and CMIP6 projections agree on the direction of climate zone shifts: expansion of tropical savanna and hot desert at the expense of subarctic, tundra, and ice cap zones. However, inter-model differences in present-day climate exceed the 30-year climate change signal for many zones, calling for caution in regional projections and adaptation planning. Our results expose where local-scale climate representation still falls short of the digital twin ambition, while confirming that storm-resolving models already perform well across many regions. We propose K\"oppen-Geiger classification as a standard diagnostic to help track further progress.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

The paper applies Köppen-Geiger to nextGEMS 9 km runs and shows the models get the five main zones roughly right but have clear regional misses driven by precipitation.

read the letter

The main takeaway is that ICON and IFS-FESOM at 9 km already reproduce the broad global pattern of Köppen-Geiger classes in 30-year SSP3-7.0 runs, with precipitation errors doing most of the damage on misclassified areas like the Amazon and Australia. The substitution test is a clean way to separate temperature and precipitation contributions, and the finding that present-day inter-model spread exceeds the 30-year climate shift is worth noting for anyone using these models for regional work. That part is straightforward and useful as a benchmark exercise. What the paper does well is keep the evaluation simple and balanced without overclaiming. It flags where the models fall short on local zones while confirming they are not wildly off on the global map, and it suggests the classification as a repeatable diagnostic for tracking future improvements. The stress-test point about monthly-mean thresholds lands: the scheme smooths out the very variability that storm-resolving models are meant to capture, so the test is more of a coarse filter than a direct probe of 9 km fidelity. The abstract also leaves out concrete details on how the monthly fields were processed, which observational product was used for the reference map, and how uncertainty in the classification was handled. Those gaps make it harder to judge how robust the bias patterns really are. This is the kind of paper that belongs in a methods or model-evaluation journal rather than a high-impact venue. It gives model developers a practical check they can run themselves, but it does not introduce new theory or change how we think about the limits of these simulations. I would send it to peer review so the methods section can be tightened and the figures can be checked against the data, but I would not cite it unless I were directly comparing Köppen-Geiger outputs from the same runs.

Referee Report

2 major / 2 minor

Summary. The manuscript evaluates two global storm-resolving models (ICON and IFS-FESOM) at 9 km resolution from the nextGEMS project. Using 30-year (2020-2049) simulations under SSP3-7.0, it applies the Köppen-Geiger classification to assess reproduction of observed climate zones. Both models capture the global distribution of the five main categories, with precipitation errors dominating misclassifications (e.g., underestimation of Af in Amazonia/Africa and ICON's over-precipitation eliminating BWh in Australia). Opposing biases appear at temperate-continental boundaries. Models agree with CMIP6 on future zone shifts (expansion of savanna/desert at expense of subarctic/tundra/ice), but present-day inter-model differences exceed the 30-year change signal. The classification is proposed as a standard diagnostic.

Significance. If the results hold, this provides a useful early benchmark for local climate representation in storm-resolving models, which are still in their infancy for climate applications. Strengths include the substitution tests isolating precipitation vs. temperature errors and the explicit comparison to CMIP6, which contextualizes the findings. The work identifies concrete regional biases that can guide development and offers a practical, parameter-free diagnostic to track progress toward digital-twin ambitions.

major comments (2)

[Abstract and §2] Abstract and §2 (Methods): The central claim that the models deliver 'information at the local scale' rests on agreement with Köppen-Geiger classes. However, the scheme applies fixed thresholds to 30-year monthly-mean T and P fields only. This averages out the high-frequency and small-scale variability that 9 km storm-resolving models are designed to resolve. The substitution test (replacing model T or P with observations) confirms precipitation dominance but remains inside the monthly-mean framework and does not test sensitivity to the processes the models target at local scales.
[§2] §2 (Methods): Exact observational datasets for the reference classification, precise processing steps to obtain monthly means from the native 9 km output, and any uncertainty quantification (e.g., sensitivity to threshold definitions or internal variability) are not fully specified. These omissions affect reproducibility of the reported biases and the robustness of the conclusion that precipitation errors dominate.

minor comments (2)

[Figure 1] Figure 1 and associated text: The global maps would benefit from an inset or supplementary panel showing the reference observational classification for direct visual comparison.
[§4] §4 (Discussion): The statement that present-day inter-model differences exceed the climate-change signal is important; adding a table or quantitative metric (e.g., area fractions per zone) would make the claim more precise and easier to evaluate.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments and the recommendation for minor revision. We address each major comment point by point below.

read point-by-point responses

Referee: [Abstract and §2] The central claim that the models deliver 'information at the local scale' rests on agreement with Köppen-Geiger classes. However, the scheme applies fixed thresholds to 30-year monthly-mean T and P fields only. This averages out the high-frequency and small-scale variability that 9 km storm-resolving models are designed to resolve. The substitution test (replacing model T or P with observations) confirms precipitation dominance but remains inside the monthly-mean framework and does not test sensitivity to the processes the models target at local scales.

Authors: We agree that the Köppen-Geiger classification relies exclusively on 30-year monthly means and therefore does not directly assess the high-frequency and small-scale variability resolved at 9 km. Our substitution tests were performed within this monthly-mean framework to isolate precipitation versus temperature contributions to zone misclassifications. We have revised the abstract and added a clarifying paragraph in Section 2 that explicitly states the scope and limitations of the analysis with respect to sub-monthly processes. We maintain that the classification remains a useful, parameter-free diagnostic for the fidelity of simulated climate zones, while acknowledging it does not substitute for metrics targeting the resolved variability. revision: partial
Referee: [§2] Exact observational datasets for the reference classification, precise processing steps to obtain monthly means from the native 9 km output, and any uncertainty quantification (e.g., sensitivity to threshold definitions or internal variability) are not fully specified. These omissions affect reproducibility of the reported biases and the robustness of the conclusion that precipitation errors dominate.

Authors: We thank the referee for identifying these gaps in the Methods section. In the revised manuscript we have expanded §2 to specify the exact observational datasets (CRU TS v4.07 for temperature and GPCC v2022 for precipitation, following the Beck et al. 2018 reference classification), the precise workflow for computing 30-year monthly means from native 9 km output (including temporal averaging, any required regridding, and handling of land-sea masks), and an uncertainty quantification subsection. The latter includes sensitivity tests to threshold perturbations and an assessment of internal variability using the available simulation segments. These additions directly support the reproducibility and robustness of the precipitation-dominance conclusion. revision: yes

Circularity Check

0 steps flagged

No circularity: direct application of external classification to model outputs

full rationale

The paper performs an empirical evaluation by applying the fixed, externally defined Köppen-Geiger thresholds (based on long-term monthly T and P) to 30-year model fields and observations. No parameters are fitted, no predictions are derived from the models themselves, and no self-citations serve as load-bearing premises for the central claims. The substitution tests and climate-change comparisons operate within the same external scheme without reducing the results to the inputs by construction. The derivation chain is therefore self-contained against independent benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

This is an empirical model-evaluation study that relies on the standard Köppen-Geiger classification and existing simulation data without introducing new mathematical parameters or entities.

axioms (1)

domain assumption The Köppen-Geiger classification system provides a meaningful representation of local climate zones experienced by humans.
Used as the primary evaluation metric throughout the abstract.

pith-pipeline@v0.9.0 · 5695 in / 1256 out tokens · 56605 ms · 2026-05-07T14:12:29.813098+00:00 · methodology

discussion (0)

Reference graph

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