Task-Adaptive Parameter-Efficient Fine-Tuning for Weather Foundation Models
Authors: Shilei Cao
Hehai Lin
Jiashun Cheng
Yang Liu
Guowen Li
Xuehe Wang
Juepeng Zheng
Haoyuan Liang
Meng Jin
Chengwei Qin
Hong Cheng
Haohuan Fu
Venue: ICLR 2026 Poster (2026)
Keywords: Weather Foundation Model
Parameter-Efficient Fine-Tuning
Earth Science
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)
TL;DR: This paper introduces WeatherPEFT, a new, more efficient fine-tuning method that performs as well as full training but with fewer resources by using  task-specific adjustments and focusing on the most critical parameters.

Abstract: While recent advances in machine learning have equipped Weather Foundation Models (WFMs) with substantial generalization capabilities across diverse downstream tasks, the escalating computational requirements associated with their expanding scale increasingly hinder practical deployment. Current Parameter-Efficient Fine-Tuning (PEFT) methods, designed for vision or language tasks, fail to address the unique challenges of weather downstream tasks, such as variable heterogeneity, resolution diversity, and spatiotemporal coverage variations, leading to suboptimal performance when applied to WFMs. To bridge this gap, we introduce WeatherPEFT, a novel PEFT framework for WFMs incorporating two synergistic innovations. First, during the forward pass, Task-Adaptive Dynamic Prompting (TADP) dynamically injects the embedding weights within the encoder to the input tokens of the pre-trained backbone via internal and external pattern extraction, enabling context-aware feature recalibration for specific downstream tasks. Furthermore, during backpropagation, Stochastic Fisher-Guided Adaptive Selection (SFAS) not only leverages Fisher information to identify and update the most task-critical parameters, thereby preserving invariant pre-trained knowledge, but also introduces randomness to stabilize the selection. We demonstrate the effectiveness and efficiency of WeatherPEFT on three downstream tasks, where existing PEFT methods show significant gaps versus Full-Tuning, and WeatherPEFT achieves performance parity with Full-Tuning using fewer trainable parameters. The code of this work is available at https://github.com/ShileiCao/WeatherPEFT.

Published as a conference paper at ICLR 2026
TASK-ADAPTIN TUNING FOR WEATHER FOUNDATION MODELS
PARAMETER-EFFICE
FINE-
Jiashun Cheng", Yang Liu", Guowen Li' Xuehe Wang's Juepeng Zheng. 1.5t Haoyuan Liang', Meng Jin Chengwei Qin?, Hong Cheng', Haohuan Fu 1 Sun Yat-sen University, 'The Hong Kong University of Science and Technology (Guangzhou)
Shilei Cao'; Hehai Hehai
The Hong Kong University of Science and Technology, "The Chinese University of Hong Kong National Supercomputing Center in Shenzhen, Huawei Technologies Co., Ltd, "Tinngua University
ABSTRACT
While recent advances in machine learning have equipped Weather Foundation Models (WFMs) with substantial generalization capabilities across diverse down- stream tasks, the escalating computational requirements associated with their expanding scale increasingly hinder practical deployment. Current Parameter- Efficient Fine-Tuning (PEFT) methods, designed for vision or language tasks, fail to address the unique challenges of weather downstream tasks, such as variable heterogeneity, resolution diversity, and spatiotemporal coverage variations, lead- ing to suboptimal performance when applied to WFMs. To bridge this gap, we introduce WeatherPEFT, novel PEFT framework for WFMs incorporating synergistic innovations. First, during the forward pass, Task-Adaptive Dynamic Prompting (TADP) dynamically injects the embedding weights within the encoder to the input tokens of the pre- pre-trained backbone via internal and external pattern extraction, enabling context-aware feature recalibration for specific downstream tasks. Furthermore, during backpropagation, Stochastic Fisher-Guided Adaptive Selection (SFAS) not only leverages Fisher information to identify and update the most task-critical parameters, thereby preserving invariant pre- pre-trained knowledge, but also introduces randomness to stabilize the selection. We demonstrate the ef- fectiveness and efficiency of WeatherPEFT on three downstream tasks, where ex- isting PEFT methods show significant gaps versus Full- Full-Tuning Tuning, and WeatherPEFT achieves performance parity with Full-Tuning using fewer trainable parameters. The code of this work is available at https: /g gighhub. com/ httm/hhiiiaaa///// WeatherPEFT.
INTRODUCTION
In an era marked by intensifying global climate change, the frequency and severity extreme weather events, such as droughts (Fabian et al., 2023; Deng al., 2023) and floods (Hirabayashi et al., 2013), have been steadily increasing. Consequently, developing accurate and timely weather modeling systems is crucial for enhancing
understanding of climate change (Beddington al., 2011; Connor, 2015). For decades, physics-based models (Kimura, 2002; Lynch, 2008; Coiffier, 2011; Bauer al., 2015; Ravindra et al., 2019) have served as cornerstones for weather research. However, their computational demands, stemming from resolving complex physical constraints, present significant challenges regarding efficiency and scalability (Ren al., 2021). Over the last decade, the widespread adoption of machine learning models in weather research has led to signifi- cant advances prediction accuracy and computational efficiency (Schultz et al., 2021; Chen al., 2023c; Shi et al., 2025). Nevertheless, most of these models remain task-specific, requiring bespoke architectures and training protocols for distinct applications, limiting their generalizability.
This limitation has spurred interest in Weather Foundation Models (WFMs), large-scale pre- pre-trained models that leverage massive data to acquire generalized representations of atmospheric processes (Nguyen et al., 2023a; Bodnar et al., 2025; Schmude et al., 2024; Zhao et al., 2024b), Fine-tuning
"quual contribution.
"orresponniig author; zhengjp88 @mail sysu. edu.er

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Published as a conference paper at ICLR 2026
Downscale
Prompt,
Maski
Precipitation
Prompts
Maska
SFAS
Forecast
SEAS
TADP
WeatherPEFT
Output
Input
TADP
WeatherPFFT
Output
Input
Current global atmospheric state 5.625
Current global atmospheric state at 1.40625
Current regional atmospheric state at 0.250
F Fature regional precipitation data at 0.25
Existing PEFT
Existing PEFT
Figure I: Unlike the uniform adaptation of existing PEFT methods, WeatherPEFT is adaptive to heterogeneous weather tasks like global downscaling (left) and regional precipitation forecasting (right) with Adapiie Dynamic Prompting and Stochastic Fisher-Guide Adaptive Selection.
is then applied to transfer the pre-trained model's knowledge, enabling it to achieve promising per- formance on downstream tasks. Nevertheless, as the scale of these models increases (Bodnar al., 2025; Schmude et al., 2024), so too does the challenge of fine-tuning them effectively and efficiently for downstream tasks. Full fine-tuning, which adjusts the entire model per task, computationally prohibitive due to escalating resource demands. Furthermore, maintaining distinct parameter sets per task creates storage bottlenecks when scaling to large models with multi-task scenarios.
In light of these challenges, Sarameter-Efficiee Fine-Tuning (PEFT) techniques prevalent in natu- ral language processing and computer vision have shown great promise (Hu al., 2022; Jia et al., 2022: Zhang et al., 2025), which seeks to potentially match even exceed the performance of full fine-tuning with a minimal number of trainable parameters updates. These methods not only facil- itate more effective adaptation novel tasks but also preserve the pre-existing knowledge within the foundation models. However, the weather downstream tasks are inherently diverse, encompass- ing wide range of objectives. This diversity poses significant challenges when adapting pre- re-ttrined models to these tasks, as the varying characteristics of each task make it difficult to apply a one-size- fits-all approach. Unlike the standardized three-channel RGB inputs of vision models the unified word embedding space of language models, meteorological data involve a wide variety of variables (e.g., temperature and humidityy, resolutions (e.g., 1.40625e and 0.25%), and spatiotemporal cover- age (e.g., global versus regional) across tasks. First, these variables are distinct physical quantities governed by fluid dynamics equations. Crucially, the correlations between these variables change depending on the task. Second, resolution in weather is not merely spatial dimension but physical regime. Changing resolution, e.g., from 5.625 to 0.25%, fundamentally alters the governing phy sics, transmitting from hydrostatic, large-scale dynamics to non-hydrostatic, convective-scale processes. Third, weather data is inherently spherical and multi dimensionnll often requiring simultaneous rea- soning across vast spatiotemporal scales. Tasks at different spatiotemporal scales impose distinctly different demands on the model's feature hierarchies.
These complexities require models adapt the varying characteristics inherent in each down- stream task. Moreover, critical limitation of most existing PEFT approaches their tendency to apply the same set of trainable parameters across different downstream tasks (Figure 1), which uni- formly updates the entire PEFT module across all inputs. These methods fail to account for the fact that different parameters may play varying roles in different tasks. For example, parameters relevant for regional precipitation forecasting may differ from those critical for meteorological downscaling. While task lask-specific selection methods exist the broader PEFT literature, they primarily focus on reducing fine-tuning costs in general domains through static selection mechanisms (Xu et al., 2021; Fu et al., 2023; Zhao al., 2024a). Consequently, they fail to dynamically recalibrate for the com- plex, variable-specific couplings and physical regime shifts that characterize meteorological data, leading to suboptimal performance.
To fill this gap, propose WeatherPEFT, novel PEFT framework for WFMs comprising Task- Adaptive Dynamic Prompting (TADP), which adapts the model's 's forward pass to task-specific char- acteristics, and Stochastic Fisher-Guided Adaptive Selection (SFAS), which governs the subsequent parameter updates during backpropagation. Since the encoder's embedding layer captures the task- specific information about input variables, resolutions, and weather phenomena, TADP extracts and
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integrates this information by transforming its weights into the input token space of the pre- tra-trained backbone. Specifically, TADP first employs three specialized adapters to model the internal patterns within the data dimension. Subsequently, it utilizes self-attention to capture the external patterns by modeling the coupling between physical variables and spatial resolution features, forming a cohesive representation. This dual approach effectively conditions the model the specific characteristics of the current task. SFAS provides a principled approach to identify optimal task-specific parameter subsets, as the relevance and impact of specific parameters can vary significantly across different weather downstream tasks. SFAS utilizes the Fisher information matrix to quantify the sensitivity of parameters to the learning objective. It further integrates an annealed stochastic component pri- oritize updates for task-critical parameters with higher possibilities while preserving foundational pre-trained knowledge. The injected randomness serves to stabilize the selection, mitigating the risk of prioritizing parameters influenced by initial noise. Our main contributions are summarized as:
This work pioneers in exploring generalizing WFMs downstream tasks. Particularly, we high- light the efficiency issues in tuning WFMs, tackling the diverse demands of weather applications.
We propose WeatherPEFT, a novel PEFT framework that integrates Task- Adaptive Dynamic Prompting (TADP) and Stochastic Fisher-guided Adaptive Selection (SFAS). TADP utilizes task- related soft prompts extracted from the encoder and SFAS filter task-adaptive parameters based on Fisher information, enabling efficient and adaptive adaptation weather downstream tasks.
We evaluate WeatherPEFT three downstream tasks where existing PEFT methods exhibit significant performance gap versus Full-Tuning. Our results demonstrate that WeatherPEFT closes this gap, achieving performance on par with Full-Tuning while using fewer trainable parameters. Remarkably, WeatherPEFT outperforms Full-Tuning on regional precipitation forecasting.
2
RELATED WORKS
2.1 WEATHER FOUNDATION MODELS
The increasing scale of available meteorological data has spurred the application of machine learning (ML) techniques in weather and climate modeling (Shi et al., 2025; Chen et al., 2023c; Schultz et al., 2021; Liu et al., 2025a;b; Zheng et al., 2025). Most notably, several models (Bi et al., 2023; Lam et al., 2023; Chen al., 2023b;a; Price et al., 2023; Chen al., 2023b) have demonstrated superior performance in medium-range weather forecasting, surpassing traditional NWPs in terms of accuracy and computational efficiency. Beyond forecasting, ML techniques show promise in various tasks, including bias correction (Gregory et al., 2024; Bretherton et al., 2022), downscaling (Mardani et al., 2024; 2023), data assimilation (Huang et al., 2024; Xiao al., 2024), and post- processing (Ashkboos et al.. 2022; Rasp & Lerch, 2018). Despite these successes, these models are typically designed for specific tasks and often trained on data in particular formats, lacking general- purpose utility for weather and climate modeling.
Foundation Models (FMs) offer a promising solution due to their ability to learn extensive prior knowledge from pre-training on large datasets (Devlin et al., 2019; Brown et al., 2020; Chowdhery et al., 2023; Radford et al., 2021; Yuan et al., 2021; Wang et al., 2023b). Therefore, recent studies have begun exploring WFMs (Bodnar et al., 2025; Nguyen et al., 2023a; Schmude et al., 2024; Zhao et al., 2024b). For instance, Aurora (Bodnar et al., 2025) is pretrained on ten sources of weather range of tasks, capable of handling weather data
datasets and has demonstrated its adaptability at arbitrary pressure levels for an arbitrary set of variables. Furthermore, Prithvi WxC (Schmude et al., 2024), a 2.3 billion parameter foundation model developed using 160 variables, demonstrates its generalization abilities across a set of challenging downstream tasks. However, as size grows, of- ten encompassing billions of parameters, the computational and storage demands increase substan- tially. This makes the standard approach of Full-Tuning for each downstream task unsustainable. Therefore, more efficient and eesource-saving fine-tuning solutions are urgently needed for WFMs.
2.2 PARAMETER- EFFICIENT TUNN-TUNNG
PEFT has emerged promising paradigm for adapting foundation models novel downstream tasks while maintaining their intrinsic knowledge (Yu et al., 2022; Hu et al., 2022; Zhou et al., 2024; Han et al., 2024; Xin et al., 2024; Zhang et al., 2025; Li & Liang, 2021). Current PEFT can be
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broadly categorized into four principal classes: Selective, Additive, Prompt-based and Reparame- terization approaches. Selective PEFT strategically optimizes partial parameter subsets of founda- tion models (Xu et al., 2021; Zaken et al., 2022: Sung et al., 2021). Additive PEFT incorporates trainable modules into the backbone and only fine-tunes these additional networks (Chen et al., 2023d; Gao et al., 2023). For instance, AdaptFormer (Chen al., 2022) incorporates lightweight down-and-up module into the model's backbone. Similarly, SSF (Lian et al., 2022) applies scaling and shifting to the features generated by each layer. Prompt-based PEFT involves learning soft con- straints in the input token or the attention layer to adapt models to new tasks like VPT (Jia et al., 2022) and Aprompt (Wang al., 2023a). Reparameterization PEFT transforms the initial parame- ters into low-dimensional representation during training while seamlessly converting the weights back to their original form for inference. LoRA (Hu et al., 2022) is a widely recognized method that decomposes the updated weight into two low-rank matrices, and DoRA (Liu et al., 2024) fur- ther advances the decomposition by separating them into a magnitude vector and a direction matrix. However, the inherent heterogeneity of weather downstream tasks, with their varied variables, res- olutions, and spatiotemporal coverage, renders conventional homogeneous PEFT approaches sub- optimal. While task-specific selection methods exist (Xu et al., 2021; Fu et al., 2023; Zhao al., 2024a), they primarily focus on reducing fine-tuning costs in general domains, often relying on static selection determined prior to training. These static mechanisms fail to dynamically recalibrate for complex, variable-specific couplings and physical regime shifts inherent in weather tasks, In con- trast, WeatherPEFT introduces dynamic, annealed selection mechanism (SFAS) combined with context-aware dynamic prompting (TADP) to explicitly address the metcorological challenges.
3 BACKGROUND AND PRELIMINARIES
Weather Downstream Tasks. This work focuses on gridded prediction tasks, which are formal- ized as spatiotemporal modeling to map input states (historical) target states (future or derived quantities). Specifically, the input is denoted as a three-dimensional array X E Vg XW where V represents the number of physical variables, such as temperature and geopotential, and H x W denotes the spatial resolution, determined by how the globe is gridded. The target is to predict an output states Y R V the fask-dependent output. For example, a global downscaling task involves mapping the 5.625 low-resolution data (32 x 64 grid points) to 1.40625 high-resolution data (128 x 256 grid points).
Similarly, V and A W are the variables and spatial resolution of
Srrameter-Effficieer Fine-Tuning The foundation model is first pre-trained on extensive source TI where T {(X), } serves as input-label pairs of each downstream task. Let the pre-trained model Ma be parametrized by 0, the goal of fine-tuning is to adapt 0 to different downstream tasks. While the standard full fine-tuning need update all parameters in 0 to obtain 0 for each downstream task T', PEFT aims to introduce minimal parameter updates A with | < 10%) For each task T the objective is to optimize the task-specific loss L with output YJ from the model Mo+20,:
data and then is fine-tuned perform a variety of downstream tasks T (
!Til
min E Exx (Mo+so (Y)1))). Yj).
Since our method is applicable to all tasks, we omit task index superscript hereafter for simplicity.
METHODS
Figure presents an overview of the proposed WeatherPEFT, which integrates two synergistic inno- vations operating at distinct stages of the fine-tuning process. The Task-Adaptive Dynamic Prompt- ing (TADP) makes the model task-aware on the forward pass, while Stochastic Fisher-Guider Adap- tive Selection (SFAS) governs the resulting parameter updates during backpropagation.
4.1 TASK-ADAPTIVE DYNAMIC PROMPTING
The encoder embedding layer serves as a rich repository of task-specific knowledge, implicitly en- coding the distinet characteristies of tasks. To explicitly extract and leverage this information,
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Frozen Tume
Decoder
Downstream tasks
Internal pattern
Adspter
Post Process
...
Embedding weight 2x0
Pat K2x0
HW-Adapter
V-Adapter
D-Adapter
Downscale
Forecast
GELU
DxPR
P4
Backbone
SFAS
@
Transformer block
Shift
Self-attention
LayerNorm
Down
Soft prompt
External pattern
Layerlorm
TADP
module
Fisher information
Stochastic intervention
Forward
Data
Embedding
Loss
Encoder
(a) The pipeline of WeatherPEFT
SFAS module
FISH Mask
Figure 2: (a) Overview of WeatherPEFT, with TADP and SFAS applied to backbone. (b) TADP generates task-aware prompts by extracting internal and external patterns from the encoder. (c) SFAS uses Fisher information and a stochastic intervention to update task-critical parameters.
propose TADP. This method employs adapters that process the embedding weights to identify both internal and external patterns. These patterns are subsequently used to generate task-aware prompts that condition the forward pass, enabling the model adapt to specific downstream applications.
Internal Pattern Extraction. The internal patterns within the encoder represent the intrinsic fea- DDxVxP, xP capture these
ture learned from data dimensions. The embedding weights relationships by mapping the input into tokens, with PA. x P., the kernel size involving spatial and resolution information, V the number of variables, 1) the hidden dimension revealing meteorologi- cal characteristics. To harness the patterns, we sequentially extract features using three specialized adapters arranged a progressive, low-to-high-leve hierarchy. Each adapter consists of a Layer- Norm layer, down-projection layer, GELU activation, and up-projection layer. Specifically,
We first process the spatial and resolution information (Pr x Pw)) that governs lo- calized interactions. The HW- WWaptepeer learns patterns from neighboring areas, thereby establishing the fundamental context of how features behave and interact across spatial locations.
HW-Adapter:
Building upon the spatially-refined features processed by the HW-Adapter, the V- Adapter models the complex interdependencies and relationships among different physical input variables (V) such as temperature and humidity, within the established spatial context.
V-Adapter:
The D-Adapter processes the abstract attributes represented by the weather charac- teristics (D). It integrates the outputs from the previous spatial and physical processing stages to capture high-level, universal patterns that holistically explain atmospheric response mechanisms.
D-Adapter:
Formally, we first flatten the spatial dimension of the embedding weights E to E E DDDVXX.PP.. Subsequently, É is passed through the adapter sequence extract the respective internal patterns:
Enw = (Adapter (E))", Ev = (Adaptery (EHw))", Ed = Adaptery(Ev)). (2)
where Eнw E IRP.P.DDXV Ey E RVXx,P&xD and Ep E PVPx are the respective outputs of adapters, and = denotes an operation that shifts the last dimension of tensor the first.
External Pattern Integration. The next step involves integrating the patterns to form a cohesive, task-specific representation. To achieve this, we capture external patterns by establishing coupling analysis between the physical quantities (V) and spatial resolution features (P,..)). We first merge the first two dimension of Ep to ED E R PVP, Pe P.X x and then apply the self-ateention operation SAA-) to Ep, followed by linear projection to generate the final soft prompt tokens Ep:
E Equery Ekay
SA(-) Softmax
Evatuer
ESA (SA(E)))* Ep (MLP(EsA)) (3)
where Eşл DXVP, Pr Ep E PXD, P the prompt length, and Eguery: Ekey- Evalue the query, key, and value, respectively. Specifically, the final step is to inject these task- -dkativaeiie prompt tokens into the backbone. The input X is first encoded into sequence of M tokens T E IR x by
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the encoder. The generated soft prompt tokens Ep are then concatenated with the input tokens T before being fed into each block the pretrained backbone. This ensures that the model processes the input data in the context of the task-specific information at every stage of computation.
4.2 STOCHASTIC AASHER-GUIDER ADAPTIVE SELECTION
The diversity of weather downstream tasks implies that parameters are not uniformly relevant across all applications. Some parameters may encode chaotic patterns for precipitation forecasting, while some focus on spatial relationships for downscaling. Consequently, we propose SFAS that adopts the Fisher information (Kirkpatrick et al., 2017) as the metric update the task-critical parameters.
A parameter's s significance can be determined by evaluating the extent to which altering the parame- ter influences the output. Consider a model parameterized by 0 E |R that defines a predictive distri- bution Po (Y)) X) with input X. The sensitivity of this distribution to small parameter perturbation 5 R can be measured using the Hillback--Leible divergence D (Po (Y (X) Po+a( Po+a(Y (YXX)..
Abbass et al. (2022); Sung et al. (2021) shows that as d + 0, the following relationship holds:
Ex D (Y P 8 Fgō O(6)),
(4)
where Fa E IR 000|0 is the Fisher information matrix (Fisher, 1922), defined as:
Fg = Ex (Ex-
(5)
Evidently, the Fisher information matrix is intrinsically linked to the change in parameters induced by the small perturbation 8. Therefore, we leverage Fisher information to guide the adaptive pa- rameter selection process. However, the [0] x |0] size of Fo renders it computationally infeasible to compute the Fisher information matrix exactly practice. Consequently, prior work often approxi- mates Fe with its diagonal matrix, or equivalently, as a vector R Especially, when we sample N data X1, Xy.... XNv from data distribution P(X). Eq. 5 can be effectively approximated as:
Po = N RY Pa
Here Fg E Rl and Eq. 6 demonstrates that a larger Po corresponds to more influential parameter. Furthermore, supervised learning framework, we have the data pairs (X,, Y), and can access the ground-truth label Y, for each Xj. 5. So we can approximate Eq. as:
N
F.
Σ Velog (Y,
N
This approximation improves computational efficiency and performance. However, due to the sig- nificant heterogeneity among weather downstream tasks, substantial noise exists during early fine- tuning, distorting Fisher information. For example, in the early epochs, parameters with high Fisher scores may capture transient noise artifacts rather than task-relevant features. To stabilize the train- ing process, we introduce an annealed stochastic component with a linear decay factor:
F6 =
ns. is
o Mac + Bo:
(8)
where 7 is the initial factor, Ma. ~ Iniform(O..1 1) is the stochastic vector, and nss and is are the current step and total step respectively. Each batch is treated as step, and during training we select the Top-k parameters with the highest Fg for optimization. The hyperparameter k governs the sparsity of the Fish Mask. The Fish Mask entries for the Top- parameters are set to one, while the rest are zero, thereby excluding less significant parameters and updating only the Top-k parameters.
5 EXPERIMENTS
We evaluate WeatherPEFT on downscaling, ensemble forecast post-processing and regional precip- itation prediction. These tasks selected to span diverse weather challenges, including variable

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Table 1: The RMSE and Mean Bias on downscaling experiments from ERAS (5.622 55.25%) to ERAS (1.40625%). We adopt the Aurora (Bodnar et al., 2025) as the foundation model and only count the trainable parameters in the backbone for all fine-tuning methods.
Method
Trainable
T2m
U10
V10
T850
7500
Parums (M)
RMSE
Mean Bias
RMSE
Mean Bias
RMSE
Mean Bias
RMSE
Mean Bias
RMSE
Mean Bias
Nearest
0.00
3.007
0
2.695
-0.039
2.717
0.038
2,010
0.007
295.495
-0.054
Bílincar
0.00
2.284
0.001
2.118
-0.038
2.176
0.038
1.439
0.007
149.662
-0.053
U-Net
20.10
1.915
-0.111
1.174
0.031
1.152
-0.033
1.773
-0.059
120.045
-11.118
ResNet
34,78
2.164
n.095
1,562
-0.087
1.513
0.013
1.513
-0.067
105. 101
10.229
VIT
315.43
2.972
0.018
1.931
-0.024
1.837
0.006
2.143
-0.218
201.027
-27.900
FoutCastNet
63.53
2,036
-0.016
1,535
-0.001
1.492
-0.003
1.494
-0.032
160.271
-4.184
ClimaX
116.65
2.512
0.043
1.691
0.005
1,649
0.009
2,000
-0.102
163 806
-2.555
Aurora
1256.27
1.227
0.006
1.126
0.006
1.134
-0.012
1.192
0.002
99.764
-0.996
Linca-Probing
0.00
1.291
0.014
1.227
-0.002
1.198
0.003
1.078
0.002
58.085
0.598
Blas Tuning
0.78
1.242
0,013
1.168
-0.033
1.148
0.000
1.026
0.004
53.049
1,108
LoRA
3.63
1.190
0.006
1.130
0.000
1.118
-0,002
0.998
-0.001
50.421
0.084
DoRA
3.75
1.228
0.010
1.140
0.001
1.120
-0.001
1.024
0.000
50,061
0.984
AdlpttFormer
4.64
1.737
00.055
1.505
-0.050
1.412
0.002
1.429
-0.083
106.667
21,029
SSF
3.92
1,180
0.009
1.106
-0.001
1.094
-0,001
0.987
0.002
48.342
0.936
VPT
3.75
1.241
0.008
1,163
-0.002
1.144
0.001
1.031
0.005
52,453
0.998
APrompe
4.34
1.228
0,010
1.151
-0.002
1.132
0.000
1.025
0.008
51.587
1.099
TADP Only
2.22
1,183
0.005
1,118
0.000
1.105
00,01
0.996
0.003
49.809
1.491
SFAS Only
1,26
1.16]
0.010
1,090
-0.001
1,081
-0.002
0.973
0.002
47,000
0.848
WeatherPEFT
3.48
1.119
0.003
1.057
0.000
1.051
-0.001
0.950
0.004
44,922
0.413
Full-Tuning
1239.94
0.906
0.002
0.882
0.000
0.884
-0.001
0.836
0.000
35.821
0.314
LoRA
57.80
1.131
0.004
1,069
0.001
1.060
0.001
0.961
0.004
45,914
1.110
DoRA
57.92
1,236
0,009
1. 1,447
-0.002
1.126
-0.001
1.030
-0.001
50.405
1.289
AdaptFormer
61.68
1.590
-0.007
1.376
-0.012
1.331
-0.003
1.282
0.006
81.465
1.739
WeatherPEFT
52.47
0.916
0.000
0.873
0..01
0.875
0.002
0.834
-0.002
35,076
0.504
variations, resolution shifts, and spatiotemporal coverage heterogeneity. Additional ablation studies on hyperparameters and fine tine-graieed comparisons are provided in Appendix B.1 and B.3.
----mmemete pre-trained foundation model with a 3D Swin Transformer U-Net backbone for the fine-tuning ex- periments. We also evaluate our method on another larger backbone, Prithvi-WxC (Schmude al., 2024), provided in Appendix B.2. More experimental settings will be discussed in Appendix F.I.
Implementation Details. We mainly leverage Aurora (Bodnar et al., 2025),
Baselines. Generally, we adopt three types of baselines. Firstly, we include models trained from scratch from vision and weather domains, i.e., U-Net (Ronneberger et al., 2015), ResNet (He et al., 2016), and VIT (Dosovitskiy et al., 2020), FourCastNet (Pathak et al., 2022), ClimaX (Nguyen et al., 2023a), and Aurora (Bodnar et al., 2025). This comparison helps to highlight the advantages of fine- tuning over training from the ground up. Secondly, to demonstrate the efficiency of PEFT, we select three conventional fine-tuning tine-tunigg approaches, including Linear-Probing Thirdly, we chose six state-of-the-ar PEFT including LoRA (Hu et al., 2022), DoRA (Liu et al., 2024), AdaptFormer (Chen et al., 2022), SSF (Lian et al., 2022), VPT (Jia et al., 2022), APrompt (Wang et al., 2023a). The architectural details are provided in the Appendix E.
and Full- Full-Tuning.
5.1 DOWNSCALING
Downscaling, the process of mapping coarse-resolution data to a higher resolution, is critical for analyzing local phenomena. In this experiment, downscale 5.625 ERAS data to 1.40625 ERÁ5 data (Hersbach et al., 2020) globally with WeatherBench dataset (Rasp et al., 2020). We simultaneously downscale the 68 atmospheric input variables test the model's ability to learn the cross-variable interactions required for accurate high-resolution outputs. Additionally, we compare WeatherPEFT with nearest and bilinear interpolation. We evaluate all methods latitude-weighted Root Mean Squared Error (RMSE) and Mean Bias. which are common metrics in downscaling works (Nguyen al., 2023b). We select 2-meter temperature (T2m), 10-meter zonal wind (U10), 10-meter meridional wind (V10), 500 hPa geopotential (Z500), and 850 hPa temperature (T850) as the primary verification fields as they collectively ensure holistic evaluation of model performance (Rasp et al., 2020). Details of the task configurations and metrics are in the Appendix F.2.
Visualizations are included in Appendix F2.. Table shows downscaling results, indicating that
Models trained from scratch generally exhibit poorer performance compared to fine-tuning ap- proaches. For example, Aurora achieves an RMSE of 1.227 for T2m, which is significantly worse
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Table 2: The CRPS and EECRPS on ensemble weather forecast post-processing with ten ensemble
members. We adopt the Aurora (Bodnar et al., 2025) as the foundation model.
Trainable Params (M)
T2m
U/1
V10
T850
7,500
Method
CRPS
EECRPS
CRPS
EECRPS
CRPS
EECRPS
CRPS
EECRPS
CRPS
EECRPS
RAW
0.00
0.732
0.250
0.889
0.304
0.899
0.304
0.719
0.246
78.222
28.766
U-Net
19.88
0,661
0.226
0.859
0.292
0.872
0.292
0.672
0.230
74.158
27.260
ResNet
33.95
0.682
0.232
0.865
0.294
0.880
0.295
0.689
0.235
75.562
27.750
VIT
311.10
0.646
0.221
0.856
0.291
0.872
0.292
0.672
0.229
73.503
26,956
FourCastNet
73.56
0.679
0.231
0.859
0.291
0.872
0.292
0.687
0.234
74,552
27.342
ClimaX
114.55
0.636
0.217
0.854
0.290
0.870
0.292
0.669
0.229
72.916
26.751
Aurora
1256.46
0.619
0.211
0.847
0.287
0.863
0.288
0.662
0.226
80.852
29,616
Linca-Probing
0.00
0.649
0.222
0.850
0.288
0.866
0.290
0.662
0.226
73.151
26.847
Bius-Tuning
0.78
0.644
0.220
0.849
0.288
0.865
0.290
0.661
0.226
73.009
26.827
LoRA
3.63
0.637
0.218
0.849
0.288
0.865
0.289
0.661
0.226
72.798
26.719
DoRA
3.75
0.638
0.218
0.847
0,287
0,864
0,289
0.660
0.225
72.827
26.735
AdaptFormer
4.64
0,647
0.221
0.862
0.294
0.878
0.295
0.666
0.227
73.312
26.869
SSF
3.92
0.629
0.215
0.847
0.287
0.862
0,289
0.659
0.225
73.025
26.832
VPT
3.75
0.635
0.217
0.846
0.287
0.862
0.288
0.659
0.225
72.883
26.774
APrompt
4.34
0.632
0.216
0.846
0.287
0.862
0.288
0.660
0.225
73.022
26,820
TADP Only
1.92
0.632
0.216
0.848
0,288
0.863
0,289
0.659
0.226
72.715
26.731
SFAS Only
1.26
0.629
0.215
0.849
0,288
0.864
0.2899
0.660
0.226
72.716
26.715
WeatherPEFT
3.18
0.618
0,211
0.844
0,286
0.860
0.287
0.657
0.224
72.701
26,665
Full- FulliTTnnig
1239.94
0.604
0.206
0.838
0,284
0.854
0.285
0.653
0.223
73.760
27.051
LoRA
57.80
0.630
0.215
0.847
0.287
0.862
0.288
0.66
0.225
72.805
26.710
DoRA
57.92
0.631
0.216
0.845
0.287
0.861
0.288
0.659
0.225
72.987
26.779
AdaptFormer
61.68
0.638
0.218
0,860
0,293
0,874
0.293
0.662
0.226
73.114
26,815
WeatherPEFT
52.18
0.601
0.205
0.838
0.284
0.854
0.286
0.650
0.222
72.745
26.683
than the 0.906 RMSE of Full-Tuning This performance gap arises from the task's nature, which necessitates simultaneous downscaling of 68 variables, posing significant challenges for models trained from scratch to effectively capture the complex interdependencies among these variables.
While the PEFT methods significantly reduce trainable parameters, they incur certain degree of accuracy degradation compared to Full-Tuning. For example, DoRA shows ~36% higher T2m RMSE compared to Full-Tuning with only 3.75M parameters (1.228 vs. 0.906). These results underscore the limitations of existing PEFT strategies in specialized scientific domains. Notably, WeatherPEFT effectively balances parameter efficiency and performance, outperforming existing PEFT baselines in terms of RMSE using the fewest parameters, with only 3.48M parameters, demonstrating its ability adapt the foundation model to the task of downscaling.
The ablation study provides further evidence of the effectiveness of our framework. TADP and SFAS individually perform well but slightly underperform versus the full WeatherPEFT, under- scoring the synergistic benefits of both modules during the forward and backpropagation passes.
To ensure comprehensive and fair comparison, we also evaluated the PEFT methods with an increased parameter budget (~4%). Even this setting, existing PEFT methods like LoRA and DoRA still fail to approach the performance of Full-Tuning. Remarkably, WeatherPEFT nearly closes the gap, achieving results nearly on par with, and in some cases better than, the Full-Tuning.
5.2 ENSEMBLE WEATHER FORECAST DOST-PROCESSING
Existing ensemble weather predictions have biases (Toth & Kalnay, 1993), prompting post- processing methods to improve forecast reliability by correcting prediction distributions. Our eval- uation uses the ENS-10 benchmark (Ashkboos et al., 2022), which pairs 10-member ECMWF IFS (ECMWF, 2022) ensemble predictions with ERA5 targets at 0.5% resolution. The dataset includes 25 surface and atmospheric variables. An additional baseline ('RAW") is included, which refers to using the raw ensemble mean and standard deviation. Performance is quantified using the Continu- ous Ranked Probability Score (CRPS) and Extreme Event Weighted Continuous Ranked Probability Score (EECRPS) (Ashkboos et al., 2022). We train the model to simultaneously correct the five same target variables as Section 5. Implementation specifics are included in the Appendix F.3.
Table 2 presents the results of post-processing across five target variables, indicating that
Unlike the downscaling task, the performance gap between Full-Tuning and training-from-sceee from-scratch baselines narrows in the post-processing task. For example, ClimaX achieves a Z500 CRPS of 72.916, marginally better than Full-Tuning's 73.760. This might suggest significant task shift
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Table 3: The SEEPS, ACC, RMSE (le-2) regional precipitation forecasting, focusing China region. We adopt the Aurora (Bodnar et al., 2025) as the foundation model and only count the trainable parameters in the backbone for all fine-tuning methods.
Trainable (M)
12 Hours ACC
24 Hours
36 Hours ACC 1
Method
SEEPS
ACC
SEEPS
RMSE
Persistence
0.00
0.695
0.265
0.371
0.720
0.168
0.387
0.855
0.088
0.416
U-Net
19.89
0.467
0.639
0.225
0.468
0.263
0.685
0.352
0
ResiNet
33.99
0.551
0.499
0.259
0.664
0.342
0.283
0.767
0.210
0.300
VIT
311.30
0.560
n.499
0.257
0.646
0.389
0.276
0.717
0.292
0.290
FoutCastNet
63.94
0.640
0.376
0.279
0.756
0.213
0.299
0.824
0.126
0.310
ClimaX
117.32
0,590
0.487
0,260
0.695
0.328
0.285
0.759
0.231
0.297
Aurora
1239.94
0.470
0,589
0.241
0,578
0.449
0.268
0.060
0.351
0.283
Linear-rroiing
0.00
0.581
0.464
0.266
0.720
0.265
0.293
0.790
0.171
0.303
Bias: Tuning
0.78
0.573
0.474
0.265
0.715
0.271
0.292
0.783
0.177
0.302
LoRA
3.63
0.495
0,592
0.24
0.634
0.415
0.273
0.723
0.294
0.289
DoRA
3.75
0.574
0.244
0.662
0.372
0.748
0.4
AdaptFormer
4.62
0.499
0.577
0.243
0.643
0.378
0.278
0.731
0.258
0.293
SSF
3.92
0.459
0.631
0.231
0,588
0.474
0.264
0.680
0.356
0
VPI
3.75
0.522
0.550
0.666
0.356
0.281
0.750
2.235
APrompt
4.34
0.521
0,554
0.249
0.650
0.387
0.277
0.733
0.271
0.292
Covpass
4.92
0.485
0.606
0.237
0.615
0.439
0.269
0.697
0.326
0.285
F-cT-TT
2.73
0.553
0.249
0.662
0.371
0.747
0.246
294
RepAdapter
3.75
0.534
0,532
0.254
0.675
0,340
0.283
0.757
0.222
0.297
SCT
3.94
0.481
0.607
0.237
0.616
0.439
0.269
0.706
0.316
0.286
Child-Tuingo
3.39
0.694
0.214
0.565
0,500
0.259
0.672
0.364
0
MoA
8.62
0.515
0.563
0.246
0.665
0.354
0.281
0.749
0.235
0.296
HydraLoRA
5.77
0,510
0.571
0,245
0.650
0,393
0.276
0.734
0.268
0.292
VeRA
0,524
0.5$1
0.663
0.744
0.293
SAM
3.39
0.421
0.673
0.220
0.598
0.457
0.267
0.704
0.299
0.289
TADP Only
2.12
0,549
0.523
0,256
0.676
0.357
0.282
0.750
0.247
0.295
SFAS Only
1.26
0.634
0.231
0.716
WeatherPEFT
3,38
0.368
0.742
0.198
0.515
0.559
0.247
0.615
0.443
0.268
Full Tuning
1246.77
0.304
0.797
0.178
0.452
0.586
0.241
0.542
0.481
0.263
LoRA
0.449
0.648
0.226
0.59
0.474
0.681
0.282
DoRA
57.92
0,512
0,576
0.244
0.659
0.383
0.277
0,746
0.254
0.293
AdaptFonner
61.68
0.458
0.623
0.232
0.599
0.438
0.269
0.691
0.324
0.286
WeathrrEFT
52.37
0.302
0.805
0.174
0.437
0.615
0.235
0.526
0.518
0.256
between the pre-training objectives and the probabilistic correction required for post-processing, which could hinder the transfer of knowledge learned during the pre-training phase.
While PEFT methods such SSF demonstrate competitive results, they still lag behind Full- Tuning. Despite the challenging task shift, WeatherPEFT achieves near-Full-Tuning performance with only 3.18M parameters. Especially on Z500. WeatherPEFT outperforms Full-Tuning (72.701 vs. 73.760 CRPS and 26.665 vs. 27.051 EECRPS). This result suggests that WeatherPEFT is capable of handling the specific challenges posed by this post-procees post-procsssing task, even when the pre-training knowledge does not directly align with the task's 's variable characteristics.
Furthermore, the ablation study demonstrates the importance combining both modules, which synergistically to adapt the foundation model's parameters the specific task at hand.
Similarly, the results in the increased parameter setting further underscore method's supe- riority. WeatherPEFT, with 52. 18M parameters, not only exceeds the performance of its PEFT counterparts but also surpasses the 1.2B Full-Tuning method across most key metrics.
5.3 REGIONAL PRECIPITATION FORECASTING
Precipitation forecasting is vital for agriculture, water management, and disaster prevention. How- ever, global predictions are often unfeasible, especially with only regional data available. To address this, we formulate regional precipitation forecasting task to predict the future sour accumula- tion of total precipitation (TP-6hr) based on the regional weather conditions. For this task, we intro- duce new dataset ERA5-CH from the ERA5 data at 0.25%, which includes five surface variables and five upper variables but focuses exclusively on the China region. Following WeatherBench2 (Rasp et al., 2024), we employ the latitude-weighted Stable Equitable Error in Probability Space (SEEPS) (Rodwell et al., 2010), Anomaly Correlation CoeCficient(ACCC, and RMSE as the evalu- ation metries. Specifically, we focus short-term forecasting with lead times of 12, 24, and 36 Hours. "Persistence" represents utilizing the input as the prediction. Complete experimental details listed in Appendix F.4, and case study extreme precipitation is presented in Appendix B.4.

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Published as a conference paper at ICLR 2026
To rigorously evaluate WeatherPEFT, we include expanded suite of PEFT baselines, including vision PEFTs (ConyPass (Jie et al., 2024), FacT (Jie & Deng, 2023), RepAdapter (Luo et al., 2023)) and task-selective methods (SCT (Zhao et al., 2024a), Child-Tuning (Xu et al., 2021), SAM (Fu et al., 2023)), LoRA variants (Hydral oRR (Tian et al., 2024), VeRA (Kopiczko et al., 2024)), and Mixture of Adapter (MOA). Table 3 presents the following results of precipitation forecasting:
Full-Tuning significantly achieves superior performance over training-from-seeane models, con- firming that knowledge transfer from pre-training is highly effective for this task.
Moreover, standard PEFT methods show sigmificant gaps versus Tull-Tuning. For example, LoRA's 12h SEEPS is 62.8% higher than Full- Full-Tuning, indicating poorer calibration of rainfall events. This underperformance due to the unique challenges of precipitation, including its sparse nature and highly localized patterns, which conventional PEFT methods fail adequately capture. In contrast, the WeatherPEFT significantly surpasses PEFT baselines, and significantly narrows the gap with Full-Tuning when constrained to minimal parameter budget (~0.3%)
aask-ade selection methods (SCT. SAM, lines like LoRA. This validates the intuition that selecting task-relevant parameters is crucial for heterogeneous weather tasks. Despite these improvements, WeatherPEFT significantly surpasses all competitors. This confirms that adaptivity alone is insufficient and coupling it with the domain- specific context awareness provided by TADP is essential for meteorological adaptation.
consistently outperform other base-
The ablation experiments provide insights into the effectiveness of the two components in Weath- erPEFT, indicating that SEAS is critical than prompting for precipitation's 's sparse signals.
Despite the increased trainable parameters, PEFT baselines' performance improves marginally but remains inferior Full- Full-Tuning. Notably, WeatherPEFT, with ~4% parameters, even surpasses the performance of Full- Full-Tuning Tuning across all metrics. This demonstrates that our method is not only more efficient but also more effective at adapting the foundation model for this complex task.
Input
Groundtruth
AdaptFormer
SSF
WeatherPEFT
Figure 3: Visualization of a 12-hour forecast for TP-6hr over China (2020-05-20 12 UTC).
Visualization We visualize the input, ground truth, and prediction AdaptFormer, SSF, and WeatherPEFT in Figure 3 to provide intuitive comparison. The complete visualization of PEFT methods is provided in the Appendix F.4.3. It distinctly reveals that deep learning approaches em- ploying pixel-wise MAE loss exhibit over-smoothed characteristies in their precipitation predictions, which are particularly noticeable in their failure to preserve fine-grained spatial patterns. However, our proposed WeatherPEFT demonstrates superior alignment with the ground truth compared to other PEFT baselines, highlighting the importance of WeatherPEFT's task-adaptive feature.
CONCLUSION
This paper proposes WeatherPEFT, the first exploration of efficient fine-tuning for weather foun- dation models. WeatherPEFT is a novel PEFT framework that integrates two synergetic modules, iee., Task-Adaptive Dynamic Prompting (TADP) and Stochastie Fisher-Guided Adaptive Selection (SFAS). In the forward pass, TADP dynamically encodes task-specific characteristics into contex- tual prompts, enabling feature recalibration tailored to diverse metcorological inputs without altering the core pre-trained knowledge. During backpropagation, SFAS integrates randomness with Fisher information to identify and update parameters sensitive to downstream objectives with higher pos- sibilities, preserving invariant physical priors while optimizing task-critical weights. Experiment results on three downstream tasks demonstrate the effectiveness and efficiency of WeatherPEFT over existing PEFT methods, highlighting its adaptability weather-related data.
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Published as a conference paper at ICLR 2026
ACKNOWLEDGMENTS
This work was supported in part by the National Natural Science Foundation of China under Grant T2125006; in part by GuangDong Basic and Applied Basic Research Foundation under Grant 2025A1515510016; in part by Shenzhen Science and Technology Program under Grant XCXFZ22244900093779904 and Grant 1
part by the Open Research and in part by the Research Grants Coun-
Fund of Pengcheng Laboratory under N0.2025KFIIB cil of the Hong Kong Special Administrative Region, China (No. CUHK 14206625). Yang Liu supported in part by the Postdoctoral Fellowship Scheme of The Chinese University of Hong Kong.
ETHICS STATEMENT
The authors have read and adhered to the ICLR Code of Ethics. We believe this work presents major ethical concerns and offers significant societal benefits. The primary goal our research is to develop efficient methods for fine-tuning Weather Foundation Models. This work contributes positively to human well-being by making advanced weather forecasting more accessible, which is critical for applications in disaster preparedness (e.g., flood and extreme weather warnings), agri- culture, and water resource management. Our research exclusively utilizes publicly available me- teorological datasets (e.g.. ERA5 and WeatherBench), which do not contain personally identifiable or sensitive human data, thereby avoiding privacy and security issues. In line with our commitment to scientific transparency and reproducibility, we have provided our code and will make it publicly available. This work has been conducted in adherence to the ICLR Code of Ethics, with the goal of fostering responsible and beneficial scientific advancement.
REPRODUCIBILITY STATEMENT
We are committed to ensuring the reproducibility of our research. Source code and a README. file with detailed instructions for environment setup, data preparation, and script execution avail- able at httpt::t///.. htt/tgttttt tuub. com/ Shi leiCao/ CCaa//Wetthii herahhr and also provided in the supple- mentary material. The appendix offers comprehensive details support our claims. Appendix describes the implementation of our proposed WeatherPEFT and all bascline models. Appendix F details the setup for each downstream task, including data sources, problem settings, and formal definitions for all evaluation metrics. Furthermore, Appendix B presents extensive hyperparameter ablation studies and generalization study to justify our main experimental choices and demonstrate the robustness of our method.
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Task-Adaptive Parameter-Efficien Fine-Tuning for Weather Foundation Models
Appendix
CONTENTS
Overview
20
Additional Studies
20
B.1 Hyperparameter Ablation Study
20
B.2 Backbone Generalization Study
22
B.3 Module Fine-grained Comparison Study
22
B.4 Real-world Case Study
23
B.5 Synergistic Analysis
24
B.6 Computational Efficiency Analysis
24
B.7 Additional Domain Specificity Analysis
24
C Discussion
26
D Use of Large Language Models (LLMs)
27
Additional Model Implementation Details
27
E.1
Training-from from--cratcch Model Architectures
27
E.1.1
ResNet
27
E.1.2
U-net
28
E.1.3
VIT
28
E.1.4
FourCastNet
28
E.1.5
ClimaX
29
E.1.6
Aurora
29
E.1.7
Prithxi WxC
30
E.2
PEFT Methods
30
E.2.1
WeatherPEFT
30
E.2.2
Other PEFT baselines
30
Additional Downstream Task Details
30
F.1
Experimental Settings
30
F22
Downscaling
E2.1
Data
31
F2.2
Problem Setting
32
F2.3
Visualization
32
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F3
Ensemble Weather Forecast Post- Poss--rocessing
35
F3.1
Data
35
F.3.2
Problem Setting
35
E.4
Regional Precipitation Forecasting
36
F.4.1
Data
36
F.4.2
Problem Setting.
................................
36
F.4.3
Visualization
36
................................
F.5
Metrics
................................
................................
37
E.5.1
Root Mean Squared Error (RMSE)
37
...............
F.5.2
Mean Bias
37
F.5.3
Continuous Ranked Probability Score (CRPS)
37
F.5.4
Anomaly Correlation Coefficient (ACC)
38
F.5.5
Extreme Event Weighted Continuous Ranked Probability Score (EECRPS)
38
F.5.6
Stable Equitable Error in Probability Space (SEEPS)
38
E.5.7
Threat Score (TS)
39
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A OVERVIEW
We provide additional details and analysis in this technical Appendix. In Section B, we furnish additional studies on hyperparameter, backbone, module fine- seeeggraied comparison, and real-workd case. In Section C, we discuss the limitations and prospective directions of our research. In Section E, we provide model implementation details on WeatherPEFT and other methodologies. Section F, we furnish additional details and visualization examples for the downstream tasks.
B ADDITIONAL STUDIES
B.1 HYPERPARAMETER ABLATION STUDY
Ablation study on key hyperparameters for the regional precipitation forecasting task, using Aurora (Bodnar et al., 2025) as the foundation model. The analyzed hyperparameters include the rank (r) for LoRA (Hu al., 2022), the parameter selection percentage (k) and initial linear decay factor (7) for SFAS of WcatherPEFT, and the number of soft prompt tokens (P) with adapter hidden dimensions (HW,, V4, D2) for TADP of WeatherPEFT.
Table 4:
Trainable
12 Hours
24 Hours
36 Hours
Hyperpurameter
Params (M)
SEEPS ACC1 RMSE
SEEPS ACC f RMSE SEEPS
ACC 1 RMSE
Full-Tuning
1246.77
0.304
0.797
0.178
0.452
0.586
0.241
0.542
0.481
0.263
LoRA-r= 256
92.01
0.495
0.591
0.241
0.633
0.423
0.272
0.716
0.306
0.288
LoRA-r = 160
57.80
0.449
0.648
0.226
0.590
0.474
0.263
0.681
0.353
0.282
LoRA-r = 128
46.39
0.491
0.592
0.240
0.627
0.425
0.271
0.714
0.307
0.288
LoRA-r = 64
23.59
0.479
0.606
0.237
0.641
0.403
0.274
0.728
0.282
0.290
LoRA-r =
3.63
0.495
0.592
0.240
0.634
0.415
0.273
0.723
0.294
0.289
k 0.040
52.37
0.302
0.805
0.174
0.437
0.615
0.235
0.526
0.518
0.256
k 0.035
46.09
0.303
0.804
0.175
0.439
0.615
0.235
0.528
0.520
0.256
k 0.030
39.81
0.305
0.803
0.175
0.440
0.616
0.234
0.530
0.519
0.255
k 0.025
33.53
0.306
0,803
0.175
0.441
0.616
0.234
0.532
0.520
0.255
0.020
27.25
0.309
0,802
0.176
0.444
0.617
0.234
0.535
0.519
0.255
0.015
20.96
0.312
0,800
0.177
0.448
0.617
0.234
0.540
0.516
0.256
0.010
14.68
0.315
0.796
0.178
0.453
0.614
0.234
0.548
0.514
0.256
0.005
8,40
0.328
0.785
0.182
0.468
0.604
0.237
0.565
0.499
0.258
A = 0.001
3,38
0,368
0.742
0.198
0.515
0.559
0.247
0.615
0.443
0.268
1.0
3.38
0.369
0.742
0.198
0.518
0.556
0.247
0.616
0.439
0.269
7 0.8
3,38
0.369
0.743
0.198
0.520
0.553
0.248
0.619
0.436
0.269
0.6
3.38
0.376
0.736
0.200
0.521
0.552
0.248
0.622
0.434
0.269
0.4
3.38
0.371
0.740
0.199
0.517
0.556
0.247
0.617
0.440
0.268
7 0.2 0.2
3.38
0.368
0.742
0.198
0.515
0.559
0.247
0.615
0.443
0.268
P= 100
4.98
0.376
0.736
0.200
0.524
0.550
0.248
0.622
0.434
0.269
P= 80
4.58
0,381
0.728
0.202
0.526
0.548
0.249
0.622
0.438
0.269
P= 60
4.18
0.375
0.736
0.200
0.529
0.544
0.250
0.631
0.422
0.272
p= 40
3,78
0.400
0.707
0,209
0,545
0.528
0.253
0.645
0.409
0.273
P = 20
3,38
0.368
0.742
0.198
0.515
0.559
0.247
0.615
0.443
0.268
P 10
2,98
0.387
0.720
0.205
0.531
0.544
0.250
0.630
0.428
0.270
IIW,, Va, Da, 32, 13, 512
27.87
0.376
0.736
0.200
0.521
0.552
0.248
0.619
0.437
0.269
HW,, Vn, Da 8,
3.38
0.368
0.742
0.198
0.515
0.559
0.247
0.615
0.443
0.268
To rigorously assess the impact key hyperparameters within WeatherPEFT, we conduct an abla- tion study on the regional precipitation forecasting task, with results presented in Table 4. First, we investigate the influence k; the percentage of selected parameters in SFAS. The findings reveal that WeatherPEFT can achieve performance comparable to, and even superior to, full fine-tuning (1246.77M parameters) using only approximately 3% the trainable parameters. With k 0.030, yielding 39.81M parameters, observe SEEPS/ACC/RMSE of 0.0.440/0.61 1...4400.61660.224. 234 for the 24- hour forecast, versus Full-Tuning's 1452/0.586/0..0 241. Additionally, a trend indicates that as k in- creases, model performance generally improves across all forecast horizons (12, 24, and 36 hours). However, the magnitude of these improvements diminishes with larger values, suggesting a point of diminishing returns where adding more trainable parameters yields only marginal gains. For
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Table 5: Ablation study key hyperparameters for the downscaling task, using Aurora (Bodnar et al., 2025) as the foundation model. The analyzed hyperparameters include parameter selection percentage (k) for SFAS of WeatherPEFT.
Trainable
T2m
C10
V10
T850
2500
Hyperparameter Parums (M)
RMSE Mean Bius
RMSE
Mean Bias
RMSE
Mean Bias
RMSE
Mean Bias
RMSE
Mean Bias
Pull-Tuning
1239.94
0,906
0.002
0.882
0,000
0.884
-0.001
0,836
0.000
35,821
0.314
& = 0.04
52.47
0.916
0.000
0.873
-0.001
0.875
-0.002
0.834
-0.002
35.076
0.504
k
39.91
0.929
0.000
0.882
-0.001
0.883
-0.002
0.840
-0.002
35.511
0.502
& = 0.02
27.34
0.949
-0.002
0.898
-0.002
0.898
-0.002
0.851
-0.001
36,284
0.630
& = 0.01
14.82
0.987
-0.001
0.928
-0.001
0.927
-0.003
0.869
-0.002
37.826
0.355
== 0.001
3.48
1.119
0.003
1.057
0.000
1.051
-0.001
0.950
0.004
44.922
0.413
Table 6: Ablation study on key hyperparameters for the ensemble weather forecast post-processing task, using Aurora (Bodnar al., 2025) as the foundation model. The analyzed hyperparameters
include parameter selection percentage (k) for SFAS of WeatherPEFT.
Trainable Params (M)
T2m
U10
VIO
T850
7,500
Hyperparameter
CRPS EECRPS
CRPS
EECRPS
CRPS
EECRPS
CRPS
EECRPS
CRPS
EECRPS
Full-Tuning
1239.94
0.604
0.206
0.838
0.284
0.854
0.285
0.653
0.223
73.760
27.051
k= 0.04
$2.47
0.601
0.205
0.838
0.284
0.854
0.286
0.650
0.222
72.745
26.683
k= 0.03
39.91
0.605
0.207
0.838
0.284
0.854
0.285
0.652
0.223
74.102
27.247
k= 0.02
27.34
0.606
0.207
0.839
0.284
0.855
0,286
0.652
0.223
73.757
27.082
k= 0.01
14.82
0.608
0.208
0.841
0.285
0.857
0.287
0.654
0.223
73.438
26.958
& 0.001
3.48
0.618
0.211
0.844
0.286
0.860
0.287
0.657
0.224
72.701
26.665
fair comparisons with other PEFT methodologies in this paper, we select k=0.001 for most of the experiments, ensuring a comparable parameter budget. To unleash the potential of WeatherPEFT and ensure a fair comparison with Full-Tuning, we supplement experiments with set to 0.04. To explicitly validate this consistency across all tasks, have conducted the same ablation study on the hyperparameter & for the other two downstream tasks: Downscaling and Ensemble Weather Forecast Post- Post-Processing. The results are presented in Tables 5 and 6, which demonstrate clear and consistent trend across two tasks. WeatherPEFTT's performance scales with trainable parameters, matching or surpassing Full-Tuning when using 3% of the model's parameters. Beyond this, perfor- mance gains gradually plateau. Additionally, we conduct a hyperparameter sweep on LoRA's rank on Aurora. The results on the precipitation forecasting task show LoRA's performance is insensitive to its parameter count and remains significantly inferior to WeatherPEFT even when it uses fewer parameters. This confirms that WeatherPEFT's superiority stems from fundamental architectural advantage, not from suboptimal baseline tuning.
Furthermore, our ablation on 7, the initial value of the linear decay factor in SFAS, demonstrates that the model exhibits relative insensitivity to this hyperparameter, with 0.2 yielding the opti- mal or jointly optimal results across most metrics and forecast horizons. This could be attributed to the weights progressively decaying towards zero, making the initial value of y less critical to the final results. Regarding the prompt length in TADP, experiments show that increasing the num- ber of soft prompt tokens beyond P=20 does not lead to further performance improvements and, in some cases, results in slight degradation (e.g.. 12-hour SEEPS increased from 0.368 at P 20 to 0.400 P=40). Given that longer prompts also increase the trainable parameter count (from 3.38M at P 20 to 4.98M at P = 100), P = 20 is identified as the most reasonable setting for this task, suggesting that excessive prompt lengths may introduce redundant parameters or make optimiza- tion more challenging without contributing additional descriptive power. Finally, the ablation on the hidden dimensions (HW,, h. Vz, Dr)) of the three adapters in TADP indicates that increasing these dimensions from a compact (8, 6. 16) to a larger (32, 13, 512) configuration (i.e. forgoing dimen- sionality reduction to retain dimensions of the original features), which drastically increase trainable parameters from 3.38M to 27.87M, do not yield performance benefits and, in fact, led to decline in metrics. This suggests that larger adapter capacities may be prone to overfitting on the down- stream task or are not necessary for capturing the task-specific information for regional precipitation forecasting, making the smaller dimensions more efficient and effective. These analyses affirm the selected hyperparameter values for achieving a strong balance between performance and efficiency.
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B.2 BACKBONE GENERALIZATION STUDY
Generalization study on the backbone for regional precipitation forecasting in the China region. Performance is evaluated using SEEPS, ACC, and RMSE (Ie-2). Prithvi-WxC (Schmude al., 2024) adopted as the foundation model, and for fine-tuning methods, we report only the trainable parameters within the backbone.
Table 7:
Trainable
12 Hours
24 Hours
36 Hours
Method
Params (M)
SEEPS
ACC
RMSE
SEEPS
ACC
RMSE
SEEPS
ACC
RMSE
Prithvi WxC
1979.10 1978.47
0.435
0.649
0.226
0.542
0.505
0.259
0.630
0,404
0.275
Full-Tuming
0.398
0.678
0.218
0.517
0.521
0.256
0.604
0.419
0.273
LoRA
86.47
0.647
0.406
0.273
0.760
0.231
0.297
0.813
0.149
0.307
WeatherPEFT
81.99
0.405
0.678
0.218
0.523
0.522
0.255
0.605
0.428
0.272
In WeatherPEFT, SFAS is universally applicable to any model trained with gradient-based optimiza- tion. Regarding TADP, its core concept involves applying series of projection transformations to the encoder's embedding space. This extracts task-specific representations, which are concatenated as soft prompts to the input each layer in the backbone model. TADP offers broad applicability across diverse settings due to three key factors:
Unified Operation Target: Embedding space is irrespective of architecture (e.g., Transformers (Vaswani et al., 2017), Convolutional Neural Networks (Krizhevsky et al., 2012). or Graph Neural Networks Scarselli et al. (2008)), and all models involve an embedding operation mapping in- put data to a continuous feature space for subsequent computation. Consequently, TADP can be applied to various embedding networks/encoders by identifying their corresponding embedding weight matrices.
Consistent Feature Processing Across Architectures: Fundamentally, diverse model architec- tures perform multi-layered computations on input feature vectors to produce outputs. TADP concatenates soft prompts the input feature map at each layer. Therefore, adapting TADP to different backbones simply requires minor adjustments based on the specific characteristies of the extracted feature maps.
Handling multi-modal inputs typically involves transitioning from a single-modal encoder to multiple single-modal encoders. TADP can integrate the embed- ding weight of multi-modal encoders. Task-specific representations are subsequently derived from this integrated space and concatenated soft prompts to the backbone's 's inputs at each layer.
Extension to Multi-modal Inputs:
In summary, WeatherPEFT demonstrates strong generalization capability across variations in model architecture, embedding methods, and input modalities. To provide concrete evidence for these claims, we further evaluate our method on different, larger foundation model: Prithvi-WxC (Schmude et al.. 2024). The results on the regional precipitation forecasting task are shown in Table We note that this model is pre-trained on data sources that are more dissimilar our downstream tasks compared to Aurora (Bodnar et al., 2025), which makes effective tine-tuning more challenging. As the table demonstrates, WeathenPEFT still achieves similar performance, matching Full-Tuning using only 4% of the parameters. Crucially, the generic PEFT baseline, LoRA, performs very poorly on this architecture. This result strongly underscores the necessity weather-specific and adaptive PEFT method like WeatherPEFT, generic approaches are not guaranteed to be effective across different WFMs.
B.3 MODULE FINN-GRAINED COMPARISON STUDY
To precisely evaluate the individual mechanisms of WeatherPEFT, conduct fine-grained ab- lation study on the downscaling task (Table 8), dissecting components of Task-Adaptive Dynamic Prompting (TADP) and Stochastic Fisher-Guided Adaptive Selection (SFAS).
Within TADP, ablating either the 'Internal' pattern extraction (designed for task-specific physical constraints) the 'External' pattern extraction (for coupling physical quantities with spatial resolu- tion features) consistently leads to performance degradation compared to the full WeatherPEFT. For instance, T2m RMSE increases from 1.119 in the full model to 1.140 (w/o Internal) and 1.130 (w/o
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Table 8: Fine-grained Ablation study on TADP and SFAS. 'External' and 'Internal' represent the external and internal pattern extraction in TADP, while 'Randomness' denotes the stochastic com-
ponent in SFAS. We adopt the Aurora (Bodnar al., 2025) as the foundation model. Experiments are done on the downscaling task under the limited (top) and increased (bottom) parameter budgets.
T2m
U10
V10
T850
Z500
Method
RMSE Mean Bias
RMSE Mean Bias
RMSE
Mean Bias
RMSE
Mean Bias
RMSE
Mean Bias
w/o Internal
1.140
0.007
1.076
0.001
1.069
-0.002
0.964
0.004
46.027
1.048
w/o External
1,130
0.006
1.068
0.001
1.062
-0.003
0.958
0.004
45.292
0.787
w/o Randomness
1.130
0.005
1.069
0.000
1.062
0.000
0.956
0.005
45.808
0.714
WeatherPEFT
1.119
0.003
1.057
0.000
1.051
-0.001
0.950
0.004
44.922
0.413
w/o Internal
0,970
0.000
0,913
-0.002
0.912
-0.002
0.860
-0.002
36.870
0.611
w/o External
0.958
0.000
0.903
-0.001
0.903
-0.003
0.854
-0.001
36.415
0.640
w/o Randomness
0.954
0.000
0.900
-0.002
0.901
-0.002
0.852
-0.001
36.277
0.620
WeatherPEFT
0.916
0.000
0.873
-0.001
0.875
-0.002
0.834
-0.002
35.076
0.504
External), highlighting the importance of these components for adapting to input data characteris- tics, particularly vital for downscaling. Similarly, for SFAS, removing the 'Randomness' (stochastic component), intended to stabilize parameter selection, results in higher RMSE values for most vari- ables (e.g. T2m RMSE increased to 1.130), underscoring the need for stabilizing the parameter selection. However, we observe that in the low-paramete regime, the performance differences, dis- tinct yet relatively small. This phenomenon is likely attributable "performance saturation", where the optimization landscape is tightly constrained by the minimal trainable parameter budget, com- pressing the variance between methods. The results of larger-parameter setting demonstrate that the performance gaps become significantly more pronounced as capacity increases. These findings collectively demonstrate that the Internal and External pattern extraction mechanisms are essential for robust scaling. They allow the model to efficiently utilize additional capacity to capture complex meteorological dynamies, preventing the premature plateauing observed in the ablated variants.
The complete WeatherPEFT consistently achieves the overall best performance (e.g., lowest RMSE for T2m, U10, T850, Z500). This demonstrates that each evaluated sub-component contributes meaningfully and synergistically to WeatherPEFT's robust and efficient adaptation capabilities.
B.4 REAL-WORLD CASE STUDY
Real-world case study on the extreme 2020 China Mei-yu flood event. Performance is evaluated using the 50th and 75th percentile Threat Score (TS) and SEEPS with forecast initialized from 7.1 12:00 on the China region. Aurora (Bodnar et al., 2025) adopted as the foundation model, and for fine-tuning methods, we report only the trainable parameters within the backbone.
Table 9:
Trainable
12 Hours
24 Hours 75%TS SEEPS 1 50% TS 75STS SEEPS J.
36 Hours
Method
Params (M)
505TS
75%TS SEEPS
50%TS
Full- Tull-Tuning
1246.77
0.64
0.50
0.34
0.70
0.45
0.67
0.57
0.34
0.68
LoRA
57.80
0.58
0.37
0.49
0.68
0.34
0.86
0.52
0.26
0.83
DoRA
57.92
0.54
0.32
0.55
0.65
0.31
0.89
0.49
0.19
0.94
AdaptFormer
61.68
0.59
0.40
0.45
0.68
0.36
0.83
0.54
0.25
0.82
WeatherPEFT
52.37
0.65
0.50
0.34
0.72
0.46
0.67
0.58
0.37
0.66
To demonstrate practical utility, we further conduct a case study on the extreme 2020 China Mei-yu (plum rain) flood, which is documented as a period of Volonté al., 2021). We initialize forecast at 12:00 UTC on July 1, 2020, during an intensely ac- tive phase of this event, evaluating performance with decision-relevant metrics such as the 50th and 75th percentile Threat Score (TS) and SEEPS. The results in Table show that with only 4% of the parameters, WeatherPEFT's performance on heavy rainfall forecasts is comparable to Full-Tuning. Crucially, it also outperforms the generic PEFT baselines, including LoRA (Hu et al., 2022), DoRA (Liu et al., 2024), and AdaptFormer (Chen et al., 2022), despite their larger number of trainable pa- rameters. This demonstrates that our method's 's targeted approach offers tangible efficiency benefits for world extreme event prediction.
flooding (Ding et al., 2021;
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B.5 SYNERGISTIC ANALYSIS
Table 10: Synergistic analysis study the backbone for regional precipitation forecasting in the
China region. Performance is evaluated using SEEPS, ACC, and RMSE (1c-2). Aurora (Bodnar
al.. 2025) adopted as the foundation model, and for fine-tuning methods, we report only the
trainable parameters within the backbone.
Trainable Params (M)
12 Hours
24 Hours
36 Hours
Method
SEEPS
ACC
RMSE
SEEPS
ACC1 f
RMSE
SEEPS
ACC
RMSE
AdaptFormer+
5.88
0.475
0.236
0.617
0.419
0.272
0.705
0.302
0.288
LoRA4SFAS
4.89
0.446
0.647
0.227
0.592
0.464
0.265
0.701
0.316
0.286
VPT+SFAS
5.01
0.395
0.708
0.209
0.537
0.533
0.252
0.639
0.41
0.273
WeatherPEFT
3.38
0.368
0.742
0.198
0.515
0.559
0.247
0.615
0.443
0.268
To rigorously validate whether the proposed TADP module provides significant architectural value beyond simply applying sparse adaptive parameter selection to existing methods, we conduct a com- parative study. We integrate the proposed SFAS mechanism with representative generic PEFT meth- ods, including LoRA (Hu et al., 2022), AdaptFormer (Chen et al., 2022), and VPT (Jia et al., 2022), and compare them with WeatherPEFT on the Regional Precipitation Forecasting task.
As presented in the Table 10, simply adding SFAS to generic adapters yields suboptimal results compared WeatherPEFT. While adding SFAS to methods like VPT does improve performance relative to their standard counterparts (Table 3), they still consistently lag behind WeatherPEFT. Notably, WeatherPEFT achieves the best performance while utilizing fewer parameters compared to the combinatorial baselines. These results suggest that generic adapters, even when optimized with grsher-guided selection, fail to adequately capture the complex variable-specific couplings and physical regime shifts inherent in weather data. By explicitly modeling internal and external patterns through TADP, WeatherPEFT provides a more effective initialization for the selection process. This empirically demonstrates that TADP is not merely supplementary module but critical architec- tural component that works synergistically with SFAS to achieve superior adaptation.
B.6 COMPUTATIONAL EFFICIENCY ANALYSIS
Table 11: Comparison of training times across different tasks.
Training Time
Methods
Downscaling
Post-Processing Precipitation Forecasting
LoRA
5h09m
1h09m
1 hh42m
AdaptFormer
5h05m
1hs06m
1 h40m
Ours
5h33m
1h20m
1h58m
The sequential implementation of the three specialized adapters in TADP and the parameter selec- tion mechanism in SFAS might introduce a degree of computational overhead compared to simpler techniques. To quantitatively evaluate this trade-off between algorithmic complexity and computa- tional efficiency, we measure the total training time for WeatherPEFT against representative PEFTs (LoRA (Hu al., 2022) and AdaptFormer (Chen et al., 2022)) across all three downstream tasks.
As shown in the Table 11, WeatherPEFT incurs a modest training time increase of approximately 10% compared to LoRA. This marginal increase in wall-clock training time highly favorable trade-off given the substantial performance gains demonstrated in the main experiments. It enables WFMs to accurately solve complex downstream tasks where generic, faster PEFT methods fail to capture the necessary physical dynamics.
B.7 ADDITIONAL DOMAIN SPECIFICITY ANALYSIS
To verify that the performance gains of WeatherPEFT stem from addressing meteorological chal- lenges, we evaluate it standard vision task. Specifically, we conduct experiments the Cityscapes (Cordts et al., 2016) + ACDC (Sakaridis et al., 2021) domain generalization bench- mark for semantic segmentation, which encompasses the Night, Snow, Fog, and Rain as the target
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Table 12: The mloU (%) on Cityscapes to ACDC domain generalization benchmark for semantic
segmentation. We adopt the DINOv2-L (Oquab et al., 2024) as the foundation model.
Trainable
ACDC (Target)
Methods
Mean
Params (M)
Night
Snow
Fog
Rain
Full-Tuning
304.20
52.4
70.5
80.9
74.4
69.5
Linear-Probing
0.00
54.3
69.3
79.1
68.0
67.6
Convpass
3.64
56.0
71.7
80.2
74.9
70.7
FacT-TT
2.85
56.1
71.3
81.0
72.9
70.3
MOA
6.39
53.2
70.6
80.3
72.8
69.3
LoRA
3.14
52.3
74.4
79.5
74.0
70.1
AdaptFormer
3.17
53.8
74.8
80.3
74.6
70.9
VPT
3.15
53.4
74.4
80.4
70.5
69.7
Ours
2.90
56.0
70.9
81.2
74.5
70.7
Natural
Specialized
Structured
Methods
params (M)
Cifar 100
Caltech
DTD
Flower102
SVIN
Sun397
Camelyon
EamoAA
Resise45
Retinoputhy
Clevr. Count
Clev--Dis
DMLab
KITTEDiss
dSpr
dSpr-Ori
SNORB
sNORB-
Averago
Full-Tuning
85.8 68.9
87.7
64.3
97.2 86.
87.4
38.8
79.7
95.7
84.2
73.9
56.3
58.6
41.7
65.5
57.5
46.7
25.7
29.1
68.9
Lincar-Probing
0.00
64.4
85.0
63.2
97.0
36.6
51.0
87.5
68.5
74.0
34.3
30.6
33.2
55.4
12.5
20.0
19.2
57.6
Convpass
0,33
72.3 91.2 72.2
99. 90.9
91.3
54.9
84.2
96.1
85.3
75.6
82.3 67.9 51.3
80.0
85,9
53.1
36.4
44,4
76.6
FacT-TK
0.17
70.6 90.6 70.8
99.1 90.7
88.6
54.1
84.8
96.2
84.5 75.7
82.6
68.2 49.8
80.7
80.8
47.4
33.2
43.0
75.6
RepAdapter
0.22 72.4
71.0
90.7
55.1
84.6
75.9
82.3
68.0
80.4
49.2
41.
76.1
SSF
0.24
69.0 92.6 75.1 99.4
91.8 90.2
52.9
87.4
95.9 87.4
75.5
75.9
62.3 53.3 80.6
77.3
54.9
29.5
37,9
75.7
SCT
0.11 75.3 91.6
72.2 99.2
91.1 91.2
55.0
85.0
96.1 86.3
76.2
81.5
65.1 51.7
80.2
75.4
46.2
33.2
45.7
76.0
LoRA
0.29
67.1 91.4 69.4
98.8
85.3
54.0
95.3
84.4
73.6
82.9
69.2
78.5
75.7
47.1
44.0
74.5
Adaptiorrmr
0.16
70.8 91.2 70.5
99.1 90.9
86.6
54.8
83.0 95.8
84.4 76.3
81.9
64.3 49.3
80.3
76.3
45.7
31.7
41.1
74.7
VPT
0.53
78.8
90.8
65.8
98.0 88.3
78.1
49.6
81.8
96.1
83.4
68.4
68.5
60.0
46.5
72.8
73.6
47.9
32.9
37.8
72.0
0.29
73.1
92.2
71.9
99.2
89.2
53.5
83.3
95.0
83.4
73.6
81.3
68.0
46.8
74.8
72.3
45.2
37.6
74.0
Table 13: Results on VTAB-IK (Zhai al., 2019) Benchmark with ViT-B/16 (Dosovitskiy al., 2020) backbone.
domains. We compare WeatherPEFT against established vision PEFT methods, including Con- vPass, FacT, MoA, LoRA, AdaptFormer, and VPT. We utilize Dinov2-L (Oquab et al., 2024) as the backbone and report the mean Intersection over Union (mloU). The results indicate that while WeatherPEFT remains competitive in the vision domain (comparable AdaptFormer), it does not demonstrate the dominant superiority observed in the weather tasks. This distinction is pivotal, veri- fying that WeatherPEFT functions not merely as an enhanced general adapter, but rather as a method specifically optimized for the unique physical semantics of weather data. Notably, the dynamic, nealed selection mechanism of SFAS, combined with context-aware dynamic prompting of TADP, provides distinct advantages in meteorological contexts.
To further investigate the generalizability and domain specificity of our approach, we also evalu- ate WeatherPEFT on the VTAB-IK benchmark (Zhai al., 2019), standard suite for evaluating transfer learning in computer vision. We utilize a ViT-B/16 (Dosovitskiy et al., 2020) backbone pre-trained on ImageNet-21k (Deng et al., 2009). We compare our method against representative visual PEFT methods, including Convpass (Jie et al., 2024), FacT (Jie & Deng, 2023), RepAdapter (Luo et al., 2023), SSF (Lian al., 2022), SCT (Zhao et al., 2024a), LoRA (Hu al., 2022), AdaptFormer (Chen et al., 2022), and VPT (Jia et al., 2022). As demonstrated in the Table 13, WeatherPEFT achieves an average accuracy of 74.0%, which is comparable to general PEFT meth- ods like LoRA and AdaptFormer. However, we observe that our method performs slightly below the SOTA on the "Structured" task group (e.g., dSprites, sNORB). We attribute this performance difference to a fundamental distinction between the VTAB-IK experimental setting and the weather domains for which our method was optimized. The core design of our TADP is to extract task- specific characteristies (e.g., variable types and physical resolutions) from the encoder's embedding layer to introduce context-aware feature recalibration. In weather tasks, the embedding layer is rich with varying physical information, allowing TADP to dynamically adapt the model the specific
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"physics" of the input. In contrast, for standard vision tasks like VTAB-IK, the embedding layers of the backbone are typically frozen and process homogeneous RGB data. In this setting, TADP extracts information from fixed layer, causing the "dynamic prompt" to effectively become static constant. This neutralizes the primary advantage of TADP's adaptivity, resulting in performance that is competitive with, but not significantly superior to, other baselines. In summary, while Weath- erPEFT is capable of handling generic tasks, its superior performance is unlocked in the weather domain, validating our motivation for a domain-specialized design that addresses meteorological challenges.
C DISCUSSION
Table 14: Scaling trends in weather foundation models.
Model
Year
Parameters
Training Resources
FourCastNet (Pathak et al., 2022)
2022
64M
16 hours; 64 A100 GPUs
Pangu (Bi et al., 2023)
2022
65M
16 days; 192 V100 GPUs
GraphCast (Lam et al., 2023)
2022
37M
28 days; 32 TPU v4 ~3 days; 80 V100 GPUs
ClimaX (Nguyen et al., 2023a)
2023
117M
FengWu (Chen al., 2023a)
2023
158M
17 days; 32 A100 GPUs
Fuxi (Chen et al., 2023b)
2023
157M
~ days; 8 A100 GPUs days; 32 A100 GPUs 64 A100 GPUs
Aurora (Bodnar et al., 2025)
2024
1.3B
Prithvi WxC (Schmude et al., 2024)
2024
2.3B
While WeatherPEFT demonstrates promising advances in PEFT for WFMs, several aspects warrant further discussion:
Scales of WFMs. First, one potential limitation of the current work pertains to the existing scale of WFMs. We are currently the early stages of developing general AI for the weather domain. Current WFMs, including Aurora (Bodnar et al., 2025) and ClimaX (Nguyen et al., 2023a), re- main in their infancy compared to mature Computer Vision (CV) or Natural Language Processing (NLP) foundation models. These models are generally smaller and less computationally demand- ing than their counterparts in NLP or CV, which might initially lessen the perceived urgency for PEFT methods in metcorological science. However, this view is rapidly being challenged by the swift expansion of WFMs. As detailed in Table 14, recent models such as Aurora (1.3B parameters) (Bodnar et al., 2025) and Prithvi WxC (2.3B parameters) (Schmude et al., 2024) already highlight a clear trajectory towards billion-paramen trend indicates that the computational and storage demands for fine-tuning will soon become unsus- tainable for many institutions. As case in point, Environment Canada reported that GPU memory constraints make it "effectively impossible" to fully fine-tune GraphCast Lam et al. (2023) on their in-house systems (Subich, 2025). In this evolving context, WeatherPEFT is presented as forward- looking initiative. Our work aims to proactively establish efficient adaptation methodologies that will be essential for the accessible and sustainable deployment of these increasingly large and com- plex future-generation weather foundation models.
scales and increasing computational requirements. This
Gnerlization of WeatherPEFT. Furthermore, WeatherPEFT has only been validated on the vaasformer-based backbone, including Aurora (Bodnar et al., 2025) and Prithvi WxC (Schmude et al., 2024), but it can be adapted to other architectures with minor modifications as discussed in Appendix B.2. Future work should prioritize its extension to other foundational architectures, such as Convolutional Neural Networks and Graph Neural Networks. Testing its performance across a broader range of downstream tasks will also be erucial for confirming its generalizability.
Trade-off between Efficiency and Performance. Moreover, it is a general observation in the PEFT field that marginal performance gap can sometimes exist when compared to the absolute ceiling achievable by exhaustive full fine-tuning when fine-tuning only a minuscule fraction of pa- rameters (~0.3%). This potential, slight differential is broadly considered an acceptable trade-off. As demonstrated in Appendix B. this performance gap for PEFT methods narrows significantly
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the budget of trainable parameters is increased to ~3%. Our method, WeatherPEFT, completely closes this gap, achieving performance that is on par with, and on certain metrics even superior select the optimal balance based on their specific ap-
to, that of full fine-tuning. Practitioners plication, choosing extreme efficiency with a small performance trade-off or allocating a modest parameter budget to achieve performance parity with full fine-tuning.
Out of Distribution Scenarios. While the WeatherPEFT framework does not include an explicit mechanism for general out-of-distribution (OOD) generalization, experimental results provide evidence of its robustness to specific distribution shifts, namely extreme weather events. This ca- pability is demonstrated by its superior performance metries designed to penalize errors on rare phenomena, including EECRPS and SEEPS. Furthermore, we evaluate WeatherPEFT on the real- world case study of the 2020 Mei-yu flood, where it achieves a high Threat Score (TS), a key decision-relevant metric. We attribute this enhanced performance to our adaptive parameter selec- tion method, SFAS. By dynamically identifying and fine-tume tine-tuning the most task-critical parameters, SFAS more effectively captures the dynamics of events in the tails of the data distribution compared to fixed PEFT strategies. This indicates promising robustness against the OOD challenges posed by extreme events.
Physical Mechanisms Incorporation. Finally, the current WeatherPEFT framework, while adapt- ing effectively through its data-driven components, does not explicitly incorporate physical mechanisms or constraints from atmospheric science directly into the PEFT process itself. Future research could investigate domain-specific PEFT methods tailored to weather and climate ap- plications to improve the performance, such as integrating physical mechanisms into the fine-tuning process (e.g., embedding conservation laws dynamical constraints).
D USE OF LARGE LANGUAGE MODELS (LLMs)
In the preparation of this manuscript, Large Language Models (LLMs) are utilized as a general- purpose assistive tool enhance the quality and clarity of the writing. The core research, experi- mental design, data analysis, and intellectual contributions remain entirely the work of the authors. The specific applications of L.MMs in this work include:
The LLM employed to review the entire text for grammatical accuracy, improve sentence structure, and ensure consistent phrasing and tone throughout the paper. This process is akin to using an advanced grammar and style checker to improve the overall readability of the manuscript.
Text Polishing and Refinement:
We use the LLM to help organize and structure our arguments. By presenting existing drafts of sections the model, receive suggestions how improve the logical transitions between paragraphs and make the overall narrative more coherent and com- pelling for the reader.
Coherence and Logical Flow:
Supplementing and Articulating Ideas: At various stages, the LLM serves as a sounding board to help supplement our thoughts. It assists in articulating complex ideas more clearly and explor- ing alternative ways to frame concepts that were already formulated by the authors. The model does not contribute to the original ideation or the generation of novel research findings but rather acts an aid to express the authors' own thoughts more effectively.
All suggestions and modifications proposed by the LLM are critically reviewed, edited, and ap- proved by the authors to ensure they accurately reflect our research and intended meaning. The final responsibility for the content of this paper rests solely with the authors.
E ADDITIONAL MODEL IMPLEMENTATION DETAILS
E. TRAINING-IROM-SCRATEE MODEL ARCHITECTURES
E. RESNET
We build the ResNet (He et al., 2016) architecture based on WeatherBench (Rasp et al., 2020; 2024) and Climateleern (Nguyen al., 2023b), where each residual block consists of two identical convo-
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lutional modules: 2D convolution - LeakyReLU with 0.3 - Batch Normalization Dropout. Table 15 shows the hyperparameters for ResNet in all of our experiments.
Table 15: Default hyperparameters of ResNet
Hyperparameter
Meaning
Value
Padding size
Padding size of each convolution layer
Kernel size
Kernel size of each convolution layer Stride of each convolution layer
Stride
Hidden dimension
The number of output channels of each residual block
256
Residual blocks
The number of residual blocks Dropout rate
28
Dropout
0.1
E.1.2 U-NET
We borrow our U-Net (Ronneberger al., 2015) implementation from ClimateLeern (Nguyen et al., 2023b). We use the following hyperparameters in the Table for UNet in Similar to ResNet, network, and all paddings are periodic in the longitude direction and zeros in the latitude direction.
our experiments. convolutional layer with kernel size of 7 at the beginning of the
Table 16: Default hyperparameters of U-net
Hyperparameter
Meaning
Value
Padding size
Padding size of each convolution layer
1
Kernel size
Kernel size of each convolution layer
3
Stride
Stride of each convolution layer
1
Hidden dimension
The number of base channels of each block The number of feature channels to scale
64
Channel multiplications
(1,2,2)
Blocks
The number of blocks
4
Use attention
If use attention in Down and Up blocks Dropout rate
False 0.1
Dropout
E.1.3 VIT
We implement the VIT (Dosovitskiy et al., 2020) architecture according ClimateLearn (Nguyen et al., 2023b), which differs from the standard ViT with some minor modifications. Specifically, the class token is removed with 1-hidden MLP prediction head incorporating, which is applied to the tokens after the last attention layer to predict the outputs. Table 17 demonstrates the hyperparameters for ViT in all of our experiments based ViT-B.
Table 17: Default hyperparameters of VIT
Hyperparameter
Meaning
Value
Padding size
The patch size to embed the input to the token The number of embedding dimension
8
Hidden dimension
1024
Depth
The number of ViT blocks
24
Heads
The number of attention heads
16
MLP ratio
Determine the hidden dimension of the MLP layer in a ViT block
4
Prediction depth
The number of layers of the prediction head
4
Drop path
For stochastic depth rate (Huang et al., 2016)
0.1
Dropout
Dropout rate
0.1
E. FOURCASTNET
The FourCastNet is implemented based on the official code of FourCastNet (Pathak et al., 2022). As shown in the Table 18, we employ the following default hyperparameters for FourCastNet.
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Table 18: Default hyperparameters of FourCastNet
Hyperparameter
Meaning
Value
Padding size
The patch size embed the input to the token
4
Sparsity threshold
The threshold of sparsity controlling in the Soft-Thresolddin
0.01
Hidden dimension
The number of embedding dimension
768
Block number
The number of AFNO (Guibas et al., 2021) blocks
Depth
The number of layers
12
MLP ratio
Determine the hidden dimension of the MLP layer in a ViT block
4
Activation layer
The activation function within each layer (Huang al., 2016)
GELU
Dropout
Dropout rate
E.1.5 CLIMAX
The ClimaX is implemented based on the official code of ClimaX (Nguyen et al., 2023a). As shown in the Table 19, we employ the following default hyperparameters for ClimaX in all of our experiments.
Table 19: Default hyperparameters of ClimaX
Hyperparameter
Meaning
Value
Padding size
The patch size to embed the input the token
Hidden dimension
The number of embedding dimension
1024
Depth
The number of ViT blocks
Heads
The number of attention heads
16
MLP ratio
Determine the hidden dimension of the MLP layer in a ViT block The number of layers of the prediction head For stochastic depth rate (Huang et al., 2016)
4
Prediction depth
2
Drop path
0.1
Dropout
Dropout rate
0.1
E. AURORA
The Aurora is implemented based the official code of Aurora (Bodnar al., 2025). As shown in the Table 20, we employ the following default hyperparameters for Aurora in all of our experiments.
Table 20: Default hyperparameters of Aurora
Hyperparameter
Meaning
Value
Patch size
The patch size to embed the input to the token Embedding dimension size
4
Hidden dimension
512
Encoder depths
The number of blocks per encoder layer
(6. 10, 8)
Decoder depths
The number of blocks per decoder layer
(8. 10, 6)
Heads
The number of attention heads
16
MLP ratio
MLP hidden dimension ratio
4.0
Encoder depth
The number of Perceiver (Jaegle et al., 2021) blocks in encoder
1
Decoder depth
The number of Perceiver (Jaegle et al., 2021) blocks in decoder The number of latent pressure levels
Latent levels
4
Window size
3D Swin window dimensions For stochastic depth rate (Huang et al., 2016)
(2, 6, 12)
Drop path
0
Dropout
Dropout rate
0
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E.1.7 PRITHXI WxC
The Prithvi WxC implemented based the official code of Prithvi-WxC (Schmude al., 2024). As shown in the Table 21, we employ the following default hyperparameters for Prithvi WxC in all of our experiments.
Table 21:
Default hyperparameters of Prithvi WxC
Hyperparameter
Mcaning
Value
Patch size
The patch size to embed the input to tokens
(2, 2)
Hidden dimension
Embedding dimension size
2560
Encoder blocks
The number of local-global transformer pairs
12
Heads
The number of attention heads
16
MLP ratio
MLP hidden dimension ratio
4.0
Drop path
For stochastic depth rate (Huang et al., 2016)
0.0
Dropout
Dropout rate
0.0
E.2 PEFT METHODS
The PEFT methods are implemented within the backbone of Aurora (Bodnar et al., 2025), which is first loaded with the official pretrained weights on over a million hours of diverse weather and climate data, and Prithvi WxC (Schmude al., 2024), which first loaded with official pretrained weights of the backbone.
E.2.1 WEATHERPEFT
As shown in Table 22, we depict some hyperparameter values in our experiment. We denote the downscaling, post-processing, and forecasting tasks as Tasks 1, 2, and respectively.
Table 22: Default hyperparameters of WeatherPEFT
Hyperparameter Module Meaning
Value (Task 1/2/3)
P
TADP
The number of soft prompt tokens
30/5/20
Ps
TADP
The heigth of the patch embedding's 's window
4/4/4
P
TADP
The width of the patch embedding window
4/4/4
V
TADP
The number of input variables
11/21/13
D
TADP
The hidden dimension of the encoder's embedding layer
512/512/512
HW,
TADP
The hidden dimension of HW-Adapter
8/8/8
Vn
TADP
The hidden dimension V-Adapter
5/10/6
Dr.
TADP
The hidden dimension of D-Adapter
16/16/16
En
TADP
The hidden dimension V P, Pu x XD -Adapter
16/16/16
k
SFAS
The percentage of selected parameters
0.001/0.001/0.000
SFAS
The initial value of linear decay factor
0.2/0.2/0.2
E.2.2 OTHER PEFT BASELINES
We implement six state-of-the-art PEFT methods, including LoRA (Hu et al., 2022), DoRA (Liu al., 2024), AdaptFormer (Chen et al., 2022). SSF (Lian al., 2022). VPT (Jia al., 2022), and APrompt (Wang et al., 2023a), based on their original paper. The default hyperparameters in our experiment are listed in Table 23.
F ADDITIONAL DOWNSTREAM TASK DETAILS
F. EXPERIMENTAL. SETTINGS
We train all the models and WeatherPEFT using the same training framework. Each model trained with the AdamW optimizer, employing weight decay of 0.05. We employ cosine learning rate
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Table 23: Default hyperparameters of PEFT baselines.
Method
Hyperparameter
Meaning
Value
LoRA
Rank
The rank of the low rank matrix
8
LoRA
Alpha
The alpha value
LoRA
Dropout
Dropout rate
0
DoRA
Rank
The rank of the low rank matrix
8
DoRA
Alpha
The alpha value
DoRA
Dropout
Dropout rate
0
AdaptFormer
Skip connection
Whether to use residual connection within the adapter
False
AdaptFormer
Mlp ratio
The ratio of down sample
0.25
AdaptFormer
Activation function
The activation function within the adapter
GELU
SSF
Layer number
The number of SSF layer
12
VPT
Prompt length
The number of soft prompt tokens
50
APrompt
Prompt length
The number of soft prompt tokens
50
APrompt
QKV length
The number of soft attention tokens
scheduler with warm-up phase during the first three epochs stabilize training. For the three distinct downstream tasks, models are trained on eight 80GB NVIDIA A800 GPUs. The specific parameters for these tasks are: learning rates of 7e-4, le-3, and 3e-3; batch sizes of 5, 1, and and 30, 10, and 15 training epochs, respectively. The approximate training times for these respective configurations are 2, and hours. the subsection, we will elaborate on the details of the implementation of model architectures and PEFT methods.
F.2 DOWNSCALING
Global weather forecasting models typically operate at coarse spatial resolutions to mitigate com- putational costs, capturing large-scale atmospheric dynamics at the expense of localized detail. However, such resolutions insufficient for analyzing regional phenomena such coastal wind patterns. Downscaling, or statistical super-resolution, addresses this limitation by enhancing coarse- grained model outputs to finer resolutions while preserving physical consistency. In this experiment, we downscale 5.625 ERA5 data to 1.40625 ERAS data (Hersbach et al., 2020) both at global scale and 6-hour intervals, leveraging the WeatherBench dataset (Rasp et al., 2020). The training involves 30 epochs over the period from 2007 to 2016, and the test is in 2017 and 2018. Following Nguyen et al. (2023a;b), we first bilinearly interpolate the input to match the resolution of the de- sired output before feeding it to the model. We use mean square error the loss function, and the overall surface loss is weighted by 0.25, while the overall upper loss weighted by 1, following (Bi et al., 2023; Bodnar et al., 2025).
F.2. DATA
Table 24 summarizes the variables for our experiments, which total 68 variables.
Table 24: ERA5 variables used our experiments. Surface represents surface variables, and Upper represents atmospheric properties at the chosen altitudes.
Type
Variable
Abbrev.
Levels
Surface
2 metre temperature
T2m
Surface
10 metre U wind component
U10
Surface
10 metre V wind component
V10
Upper
Geopotential
Z
Upper
wind component
U
50, 100, 150, 200, 250, 300, 400, 500, 600, 700,
Upper
V wind component
V
Upper
Temperature
T
850, 925, 1000
Upper
Specific humidity
Q
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F.2.2 PROBLEM SETTING
In this 5.625 ERAS data to 1.40625' downscaling experiment, the 5.625 input data 268x32x64 is first bilinearly interpolated 1.40625 data X E R following Nguyen et al. (2023a;b). The machine learning models are trained to correct the biases between the interpo- lated input data X and ground truth 1.40625 data Y E [808x32x44
F.2.3 VISCALIZATION
We visualize the input, ground truth, and prediction of seven PEFT approaches (our proposed Weath- erPEFT and six other ofae-of-thhe-rr PEFT baselines) provide an intuitive comparison for further reference.
Input
Groundtruth
LoRA RMSE: 1.289
DoRA RMSE: 1.391
AdaptFormer RMSE: 2.061
RMSE: 1.275
VPT RMSE: 1.334
APrompt RMSE: 1.315
WeatherPEFT RMSE: 1.198
Figure 4: Visualization of PEFT baselines and WeatherPEFT on the variable T2m of downscaling (2018-01-11 06 UTC).
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Input
Groundtruth
1.132
DoRA RMSE: 1.235
AdaptFormer RMSE: 1.647
SSF RMSMMME:::R 1.105
VPT RMSE: 1.170
APrompt RMSE: 1.154
WeatherPEFT RMSE: 1.049
Figure 5: Visualization of PEFT baselines and WeatherPEFT on the variable U10 of downscaling (2017-08-1) 14 06 UTC).
Input
DoRA RMSE: 1.178
AdaptFormer RMSE: 1.497
SSF RMSE: 1.077
APrompt
Figure 6: Visualization of PEFT baselines and WeatherPEFT on the variable V10 of downscaling (2017-08-L) 14 06 UTC).
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Input
Groundtruth
RMSE: 1.077
DoRA RMSE: 1.166
AdaptFormer RMSE: 1.670
SSF RMSE: 1.068
VPT RMSE: 1.127
APrompt RMSE: 1.115
WeatherPEFT 1.020
Figure 7: Visualization of PEFT baselines and WeatherPEFT on the variable T850 of downscaling (2018-01-11 06 UTC).
Input
DoRA RMSE: $4.811
AdaptFormer RMSE: 162.272
SSF RMSE: 48.945
APrompt 51.077
WeatherPEFT RMSE:
Figure 8: Visualization of PEFT baselines and WeatherPEFT on the variable Z500 of downscaling (2018-03-27 06 UTC).
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F.3 ENSEMBLE WEATHER FORECAST POST- PROCESSING
Existing ensemble weather predictions subject to systematic errors known biases (Toth & Kalnay, 1993). Therefore, post-processing approaches have been introduced to forecast skill by correcting the distribution of the ensemble weather prediction improve the reliability of weather forecasting. Our evaluation employs the ENS-10 benchmark (Ashkboos et al., 2022) for global ensemble forecast post-processing, which pairs 10-member ensemble prediction (48-hour lead time) from the ECMWF Integrated Forecasting System (IFS) (ECMWF, 2022) with ERA5 reanalysis targets at 0.5º resolution. The dataset involves two data points per week spanning 20 years, with the years 1998-2015 as the training set and 2016-2017 as the test set. Following (Ashkboos et al., 2022), we utilize the closed-form expression of the Continuous Ranked Probability Score (CRPS) as the loss function, training for 10 epochs.
F.3.1 DATA
Table 25 summarizes the variables we use for our experiments, which total 25 variables.
Table 25: ENS-10 variables used in experiments. Surface represents surface variables, and Upper represents atmospheric properties at the chosen altitudes.
Type
Variable
Abbrev. Levels
Surface
Sea surface temperature
SST
Surface
Total column water
TCW
Surface
Total column water vapor
TCWV
Surface
Convective precipitation
CP
Surface
Mean sea level pressure
MSL.
Surface
Total cloud cover
TCC
Surface
Skin temperature at surface
SKT
Surface
Total precipitation
TP
Surface
2 metre temperature
T2m
Surface
10 metre U wind component
U10
Surface
10 metre V wind component
V10
Upper
Geopotential
Z
Upper
U wind component
Upper
V wind component
V
Upper
Temperature
500, 850
Upper Specific humidity
Q
Upper
Vertical velocity
W
Upper Divergence
F.3.2 PROBLEM SETTING
For given time T, the input is a set of ensemble members X [X Each ensemble member X E x60 consists of all surface and upper variables predictions at time steps T 24h. For each target variable, the task is predict corrected cumulative distribution function (CDF) F.g at time T + 48h at each grid point (i. Following Toth & Kalnay (1993); Grönquist al. (2021), we assume Gaussian distribution on the target variable and learn the mean and stan- dard deviation of this distribution. Specifically, the model is provided with the mean and standard deviation of all variables ENS-10 at a lead time of 7 48h. The model outputs two values cor- responding to the mean and standard deviation of the target variable. To derive the corrected mean, the first output value multiplied by the ensemble member's standard deviation and added to the ensemble mean. Similarly, the corrected standard deviation is obtained by taking the exponential of the second output value and multiplying it by the ensemble standard deviation. This normalization ensures accurate calibration of the predicted distribution. We choose to minimize the Continuous Ranked Probability Score (CRPS) between the ensemble prediction and ERAS ground case, the closed-form expression of CRPS of Gaussian distribution (Ashkboos et al., 2022) can be
this
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defined as:
X
X
X (x -1) 1 2Φ
1
CRPS(P,5; X)
24
(9)
V
where i and a are the mean and standard deviation the distribution, v and á are the probability density and cumulative density function of standard Gaussian random variable, respectively.
F.4 REGIONAL PRECIPITATION FORECASTING
Precipitation forecasting plays crucial role in agriculture, water resource management, and disaster prevention (Yue et al., 2022; Ward et al., 201 1). Among fundamental atmospheric forecast variables, precipitation forecasting presents unique challenges. This is primarily attributed to the multiscale interactions involved in precipitation processes, ranging from cloud microphysies to large-scale cir- culation (Frank al., 2024), encompassing complex nonlinear dynamical, water vapor transport, and thermodynamic processes (Trenberth et al., 2003). Moreover, global predictions are not always feasible. particularly when only regional data available. In this experiment, we evaluate Weather- PEFT on regional six-hour precipitation accumulation forecasts across China, addressing scenarios where only localized observational data is available. To enable this assessment, we introduce ERAS- CH, specialized dataset derived from ERA5 reanalysis at resolution 0.25 exclusively over China. To do this, we first identified the latitude (58.3 1 N-1.5 % and longitude 174.0"E-134.000 range to form rectangular area that encapsulates China. For each data sample, we then extracted the spatial positions that fall into this range, forming ERAS-CH. We utilize the mean absolute eπror loss for training and train the model over 15 epochs. with data from 2010- 2019 serving the training set and 2020 as the test set. Both datasets are configured with a 12- 12-hour temporal resolution.
F.4.1 DATA
Table 24 summarizes the variables we use for our experiments, which total 70 variables.
Table 26: ERA5 variables used our experiments. Surface represents surface variables, and Upper represents atmospheric properties at the chosen altitudes.
Type
Variable
Abbrev.
Levels
Surface
Total precipitation of 6 hours
TP
Surface
Mean sea level pressure
MSL
Surface
2 metre temperature
T2m
Surface
10 metre wind component
U10
Surface
10 metre wind component
V10
Upper
Geopotential
Z
Upper
wind component
50, 100, 150, 200, 250, 400, 500, 600, 700.
Upper
V wind component
V
300,
Upper
Temperature
850, 925, 1000
Upper
Relative humidity
F.4.2 PROBLEM SETTING
In this regional precipitation forecasting experiment, the input X E 700x2002200 is 0.25% data with 70 variables and 240 x 240 grids. The machine learning models are trained to predict the six-hour accumulation of precipitation for three lead times of 12 hours, 24 hours, and 36 hours, which is also 0.25 data Y E fxxx40x240 240 with 240 x 240 grids.
F.4.3 VISUALIZATION
We provide the visualization PEFT baselines and WeatherPEFT on the variable TP (total precip- itation) in Figure 9.
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Input
Groundtruth
LoRA
DoRA
AdaptFormer
SSF
VPT
APrompt
WeatherPEFT
Figure 9: PEFT baselines and WeatherPEFT visualization of 12-hour forecast for TP-6hr over China (2020-05-20 UTC).
F,5 METRICS
This section defines all the evaluation metrics employ in the experiment. For arbitrarily variable, denote Y R and Y E N and Y the prediction output and the ground truth, both of which have the same shape, where Nrepresents the number of data points, H denotes the number of latitude coordinates, and W the number of longitude coordinates. Úk,k. and yk,a, indicates scalar values the prediction tensor Y and the ground truth tensor Y, respectively. The indices ki and correspond the data sample, latitude, and longitude.
F.5.1 ROOT MEAN SQUARED ERROR (RMSE)
Following WeatherBench, we define the RMSE as the mean latitude-weighted RMSE over all fore- casts for each variable:
H W
RMSE N
ΣΣ
((i)(nn. Wk.i.)
(10)
HxW
=1
where W() is is the latitude weighting factor for the latitude at 7th latitude index:
cos(lat(i))
W(1)
(11)
Nixt Σ1
Niat cost (os((a(()))
F.5.2 MEAN BIAS
Mean bias quantifies the discrepancy between the spatial average of predictions and ground truth. A positive value indicates systematic overestimation, while a negative value reflects an underestimation of the mean. The Mean Bias for each variable is defined as:
H W
ΣΣΣ (OK.A- - Bk.a.)
Mean Bias
(12)
N X W
F.5.3 CONTINUOUS RANKED PROBABILITY SCORE (CRPS)
CRPS generalizes the mean absolute error for probabilistic forecasts. Given a ground truth observa- tion y at grid-point (i,j), the CRPS for the corrected cumulative distribution function F at the same point is defined as:
CRPS(Fy, = (Fig(z) 1y5 dx,
(13)
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where 1ysz is indicator function that equals 1 if y S and 0 otherwise. This formulation quantifies the discrepancy between the predicted cumulative distribution function and the observed value, providing robust measure of probabilistic forecast accuracy. We report the mean CRPS over all grid points over the two test years.
F.5.4 ANOMALY CORRELATION COFFFICIENT (ACC)
ACC measures the spatial correlation between the anomalies of prediction Y and ground truth Y, where both are computed relative climatological baselines. Formally, ACC is defined as:
Enss (()) Ui.d.
ACC
V W(1) Eass WOW
(14)
Y-C = Y
where climatology C is the temporal mean of the ground truth data over the dataset.
F.5.5 EXTREME EVENT WEIGHTED CONTINUOUS RANKED PROBABILITY SCORE (EECRPS)
A critical objective in bias correction is reducing uncertainty during extreme weather events. To avoid conflating these events with average-case forecast skill, (Ashkboos et al., 2022) introduces a weighted version of CRPS that emphasizes extreme conditions. A widely adopted metric for quan- tifying forecast irregularity is the Extreme Forecast Index (EFI) (Lalaurette, 2003; Zsótér, 2006), which measures the deviation of ensemble forecasts relative probabilistic weather model. The EFI ranges between -1 and 1, with larger absolute values indicating greater deviation from histori- cal meteorological records. Typically, EFI magnitudes between 0.5 and 0.8 are considered unusual, while values above 0.8 signify very unusual conditions and a high likelihood of extreme weather. Given ground-truth observation y at grid-point (i,j), weight the CRPS using the absolute value of the EFI at that location, defining the Extreme Event Weighted CRPS (EECRPS) as:
EECRPS (Fej v) = (E)] x CRPS( v)-
(15)
We report the mean EECRPS over all grid points of the test years. For the calculation of EFI(i,), please refer to (Ashkboos et al., 2022)
F.5.6 STABLE EQUITABLE ERROR IN PROBABILITY SPACE (SEEPS)
Traditional deterministic metrics such as RMSE and ACC are inadequate for evaluating precipi- tation forecasts due to precipitation's 's highly skewed distribution and spatiotemporal intermittency. These limitations cause conventional metrics to favor overly smooth forecasts. Following (Rasp et al., 2020), we adopt the SEEPS score (Rodwell et al., 2010) for precipitation evaluation. SEEPS categorizes precipitation into three classes: "dry," "light," " and "heavy," discouraging smooth fore- casts while maintaining stability actoss parameter choices. For more details about the SEEPS score, please refer to (Rodwell et al., 2010). Here, we describe how we compute the SEEPS score based on (Rasp al., 2024). For every location, cumulations. The remaining precipitation values are split into light and heavy categories, with light precipitation days occurring twice as frequently as heavy ones for that location climatologically. We utilize the light-heavy threshold precomputed by (Rasp et al., 2024), which is the 2/3rd quantile of non-dry days based on climatology (Rasp et al., 2024). Foreeast-observation pairs classified into these categories based on the thresholds, generating 3 x 3 joint probability contingency table (Table 27) for each lead time.
dry threshold of 0.1 mm/day for 6 hourly
The contingency table is then multiplied by the scoring error matrix S based on the climatological occurrence of dry days for each geographical location:
1 2
P
s
0
(16)
3
2+p
2+p
where p represents the climatological probability of dry days, columns represent observed probabil- ities, and rows represent forecast probabilities. Following (Zhao al., 2024b; Rodwell al., 2010),
38

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Published as a conference paper at ICLR 2026
Table 27: 3 x 3 contingeney table of precipitation classification forecast and observation in SEEPS scores.
Probability
Observation
Category
1
2
3
P11
P12
P13
Forecast
2
Pzi
P22
P23
Psi
P32
Pas
exclude extreme climates using 0.1 <p<< 0.85 and compute area- area- rrea-weighted mean SEEPS scores. can be seen in Equation that the SEEPS error scoring matrix is uniquely determined by For rainy climate regions, where p is smaller, the lower triangular elements of the SEEPS error scoring matrix (corresponding to false negatives for "dry" conditions) are larger. For arid climate regions, where p is larger, the upper triangular elements the SEEPS error scoring matrix (corresponding to false negatives for "heavy rain") are larger. This indicates that the SEEPS error scoring matrix, which is based on the probability of precipitation occurrence (1 p). varies across different climate regions or precipitation seasons. Consequently, a key feature of SEEPS is its ability to assign dif- ferent error scores to the same forecast characteristic (e.g., missing a "heavy rain" event) depending on the climate region season. In other words, the "penalty" for forecast errors is tied to the cli- matic probability of precipitation. Thus, SEEPS automatically adapts site-specific precipitation probabilities across varying climate zones seasons.
F.5.7 THREAT SCORE (TS)
The Threat Score (TS), also known as the Critical Success Index (CSI), is a widely used verification metric in meteorology for evaluating the performance categorical forecasts, particularly for pre- cipitation events (Schaefer, 1990). It measures the fraction of correctly predicted "yes" events out of instances where the event was either predicted or observed. The TS particularly valuable as it ignores correct negatives (correctly forecasting event), making it sensitive to performance rare or localized phenomena like heavy rainfall.
To calculate the TS, forecast-observation pairs each grid point are first categorized into contin- gency table based on predefined event threshold. The categories are Hits (H), where the event was forecast occur and did occur, Misses (M), where the event not forecast to occur but did occur, and False Alarms (F), where the event was forecast to occur but did not occur. The Threat Score is then computed using the following formula:
TS H M++
(17)
The score ranges from 0 to 1, where indicates a perfect forecast. In the context of our case study percentile-hased thresholds define the precipitation events, the 2020 Mei-yu flood, evaluation moderate and heavy rainfall. Specifically, we establish allowing for a location-specio
two thresholds for each grid point based on climatology constructed from precipitation data in June and July between 2010 and 2020:
50th Percentile TS: An event is defined as precipitation exceeding the local 50th percentile the climatology.
75th Percentile TS: An event is defined as precipitation exceeding the local 75th percentile of the climatology.
This approach ensures that the metric evaluates the model's ability to predict rainfall events that significantly intense relative to the typical climate of each specific location during that season.
39


Reviews:
[{"review_id": "8LbbVJW38B", "reviewer": "Reviewer_hX2x", "summary": "WeatherPEFT introduces a novel Parameter-Efficient Fine-Tuning framework for Weather Foundation Models. It combines Task-Adaptive Dynamic Prompting for context-aware feature recalibration and Stochastic Fisher-Guided Adaptive Selection for stable parameter optimization, achieving efficient fine-tuning and performance parity with full-tuning using fewer trainable parameters.", "questions": "None", "limitations": "", "rating": null, "confidence": 5, "soundness": 1, "presentation": 2, "contribution": 1, "strengths": "- The paper is well-written as origanizing the methods and experiments clear.\n\n- This paper compares many methods, demonstrating that the experiments are extensive and comprehensive.\n\n- The appendix is clear and solid.", "weaknesses": "- The description about related works is actually incorrect from Line 078-082. First, there are a lot of task-specific PEFT methods introduced to dynamically select task-specific features to do the adaptation (SCT IJCV 2023) or implicitly selecting features (AdaptFormer, Adapter). Second, most of PEFT methods trained different sets of parameters for different downstream tasks, hence the \"apply the same set of trainable parameters across different downstream tasks\" is wrong. Then, it still unknow what is the key difference of conducting weather like downstream tasks with the normal vision tranfer tasks in VTAB. \n\n- Based on the above factual error, it indicates (1) the motivation in this paper is incorrect; (2) the literature review of this submission is not adequate and fully understood.\n\n- The SFAS module is similar to the paper \"Raise a Child in a Large Language Model: Towards Effective and Generalizable Fine-tuning\". The novelty is limited. Please be careful give the comparison.\n\n- The selected PEFT baselines are old, ConvPass, FacT, NOAH, SCT, RepAdapter are not included.\n\n- It is confusing why w/o Internal and w/o External shows minor improvements as compared with WeatherPEFT in Table 8.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "rlqs5ROLOR", "reviewer": "Reviewer_Nt4E", "summary": "They aimed to tackle the task-adaptive fine-tuning of the foundation models for weather forecasting. To deal with the domain specific challenges in weather forecasting (eg, task-specific characteristics), they proposed two PEFT ideas, specifically: i) task-adaptive dynamic prompting and ii) stochastic fisher-guided adaptive selection. The proposed PEFT method was tested on three downstream tasks/scenarios: downscaling, ensemble, and regional forecasting.", "questions": "See questions in the weakness section.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "- A novel adapter module called task-adaptive dynamic prompting looks interesting. \n- Interesting applications of PEFT of foundation models for the weather forecasting domain such as regional forecasting and ensemble forecasting.", "weaknesses": "- Arguments in Line 074-082 on why existing PEFT methods fail on weather task adaptation problems, is not very convincing since they also seem to hold for language and vision tasks (eg, task-wise varying input characteristics, different parameters play varying roles in different tasks). Are there any more convincing arguments on why the weather domain is specific?\n\n- Can the proposed TADP and SFAS be applied to general domains? like vision and language downstream task PEFT? Why is it specifically useful for weather data? Some PoC tests on general language and vision domains can be useful. Eg, the HW-, V-, D-adapters, and the external pattern integration in the proposed TADP appear to be applicable to these domains as well almost straightforwardly without modification. \n\n- MoE (eg, mixture of lora experts) is also a very powerful and topical tool for FM and PEFT. As it's not compared in the Curious to see how it would perform on the benchmarks they used as a baseline.  \n\n- SFAS seems to be the technique relatively well known in the PEFT literature. So it may not be appropriate to claim it as one of the main contributions or novelty of the paper.\n\n- The PEFT baselines they tested are a bit old, eg, beyond lora there are many more, eg, \nsparse lora, vera, or svd-based low rank selection.\n\n- Also, as sparse PEFT competing baselines, as a comparison to the proposed SFAS, the authors may need to consider various pruning methods, and the second-order sparse selection method like [R2].\n\n[R2] Zihao Fu, Haoran Yang, Anthony Man-Cho So, Wai Lam, Lidong Bing, and Nigel Collier. On the Effectiveness of Parameter-Efficient Fine-Tuning. AAAI, 2023.\n\n- Overall, since the sparse adaptive parameter selection based on Fisher info is relatively well known, the main novelty of the paper seems to be merely adding the TADP adapter module. But it is not very clear if the impact of TADP is so significant for publication in this area. Is it better than some combinations of existing PEFT and sparse adaptive parameter selection techniques that the authors did not explore but are often performed in practice? More empirical exploration may be needed together with better justification of the TADP module.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "Fs7zSNDHx2", "reviewer": "Reviewer_maT4", "summary": "The authors propose WeatherPEFT, a parameter-efficient fine-tuning method for Weather Foundation Models. The novelty in this work is to combine: (i) Task-Adaptive Dynamic Prompting (TADP), which extracts spatial, variable-level, and meteorological patterns from the encoder’s embedding weights and injects them as soft prompts into the backbone during the forward pass, and (iii) Stochastic Fisher-Guided Adaptive Selection (SFAS), which uses Fisher information (plus controlled randomness) to update only the most task-critical parameters, and preserve core pre-trained knowledge. The authors show that, altogether, these components allow WeatherPEFT to achieve performance comparable to (and sometimes better than) full fine-tuning across tasks such as downscaling, ensemble forecast post-processing, and precipitation prediction, while using significantly fewer trainable parameters than existing PEFT methods.\n\nAdmittedly, I am not an expert in Weather Foundation Models (WFM), but I believe that, aside from being able to properly judge how this work relates to previous WFM works, this lack of expertise does not affect my ability to judge the main contribution of the paper, i.e. the PEFT part.", "questions": "1. Have the authors tried to evaluate the increase of performance relative to the added complexity of their method?", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "1. I congratulate the authors on the clarity of this paper. It is extremely well structured and most of the components are very well explained.\n\n2. Climate change exerts pressure through environmental and societal impacts, including more extreme weather events like floods, droughts, and heatwaves, leading to sea-level rise, and causing water scarcity, food insecurity, and habitat destruction. The relevance of this work does not need any justification, and such techniques will be of great aid in the years to come.\n\n3. WeatherPEFT shows consistently strong results across downstream tasks.\n\n4. The authors do a great work in systematically comparing their results against strong PEFT methods and baselines, providing further support of the usefulness of their approach.", "weaknesses": "1. WeatherPEFT is a more complex technique that involves the sequential implementation of three adapters, on top of the rest of the TADP process and the SFAS. Thus, the results shown come at a computational cost.\n\n2. Although I am not asking the authors to perform more experiments, evaluating their method on further downstream tasks could be of interest for future work.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "4HsuLHRPtc", "reviewer": "Reviewer_L3dy", "summary": "Weather foundation models (WFMs) are important.\n\nPEFT failed to address the challenges of weather downstream tasks, such as variable heterogeneity, resolution diversity, and spatiotemporal coverage variations, leading to suboptimal performance when applied to WFMs.\n\nThis paper introduces WeatherPEFT, which is tailed PEFT for WFMs.", "questions": "1. Can you apply the proposed PEFT method on any dataset outside of the weather domain?\n\n2. How sensitive is the proposed HW-adaptive in weatherPEFT to the different resolution parameter?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "This paper has done comprehensive study to make the WeatherPEFT works for the weather foundation models. Compared to the standard PEFT like LoRA, WeatherPEFT surpasses them and approaches full-tuning performance even with a minimal parameter budget e.g. ~0.3%.", "weaknesses": "One concern with this work is that the proposed method lacks fair evaluation. From the proposed method, it does not seem to be unique to weather foundation models at all. It seems that the proposed method would work to some extent on the other problems too. So it seems that the evaluation only on the weather foundation model is not a fair comparison.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)