Extreme Weather Nowcasting via Local Precipitation Pattern Prediction
Authors: Chang hoon Song
Teng Yuan Chang
Youngjoon Hong
Venue: ICLR 2026 Poster (2026)
Keywords: Spatiotemporal Forecasting
Extreme Heavy Rain
Video Transformer
Primary area: learning on time series and dynamical systems

Abstract: Accurate forecasting of extreme weather events such as heavy rainfall or storms is critical for risk management and disaster mitigation. Although high-resolution radar observations have spurred extensive research on nowcasting models, precipitation nowcasting remains particularly challenging due to pronounced spatial locality, intricate fine-scale rainfall structures, and variability in forecasting horizons. 
While recent diffusion-based generative ensembles show promising results, they are computationally expensive and unsuitable for real-time applications. In contrast, deterministic models are computationally efficient but remain biased toward normal rainfall. Furthermore, the benchmark datasets commonly used in prior studies are themselves skewed--either dominated by ordinary rainfall events or restricted to extreme rainfall episodes--thereby hindering general applicability in real-world settings.
In this paper, we propose exPreCast, an efficient deterministic framework for generating finely detailed radar forecasts, and introduce a newly constructed balanced radar dataset from the Korea Meteorological Administration (KMA), which encompasses both ordinary precipitation and extreme events. Our model integrates local spatiotemporal attention, a texture-preserving cubic dual upsampling decoder, and a temporal extractor to flexibly adjust forecasting horizons. Experiments on established benchmarks (SEVIR and MeteoNet) as well as on the balanced KMA dataset demonstrate that our approach achieves state-of-the-art performance, delivering accurate and reliable nowcasts across both normal and extreme rainfall regimes.

Published as a conference paper at ICLR 2026
EXTREME WEATHER NOWCASTING VIA LOCAL PRE- CIPITATION PATTERN PREDICTION
Changhoon Song"& & Teng-Yuan Yuan Chang"
Research Institute of Mathematics
Scoul National University
Seoul, Republic of Korea
[changhoon. www.ighhoonsonn93.....o tony8900443 @sn.u acr
Youngjoon Hongt
Department of Mathematical Sciences & Research Institute of Mathematics
Seoul National University
Center for Al and Natural Sciences, Korea Institute for Advanced Study (KIAS) Seoul, Republic of Korea
hongy snu ac.kk
ABSTRACT
Accurate forecasting extreme weather events such as heavy rainfall or storms is critical for risk management and disaster mitigation. Although high-resolution radar observations have spurred extensive research on nowcasting models, pre- cipitation nowcasting remains particularly challenging due to pronounced spa- tial locality, intricate fine-scale rainfall structures, and variability in forecasting horizons. While recent diffusion-brat based generative ensembles show promising results, they are computationally expensive and unsuitable for real-time applica- tions. In contrast, deterministic models are computationally efficient but remain biased toward normal rainfall. Furthermore, the benchmark datasets commonly used in prior studies are themselves skewed-either dominated by ordinary rain- fall events or restricted to extreme rainfall episodes-thereby hindering general applicability in real-world settings. this paper, we propose exPreCast, an efli- cient deterministic framework for generating finely detailed radar forecasts, and introduce newly constructed balanced radar dataset from the Korea Meteorologi- cal Administration (KMA), which encompasses both ordinary precipitation and extreme events. Our model integrates local spatiotemporal attention, texture- preserving cubic dual upsampling decoder, and temporal extractor to flexibly adjust forecasting horizons. Experiments on established benchmarks (SEVIR and MeteoNet) well the balanced KMA dataset demonstrate that approach achieves state-of-the- performance, delivering accurate and reliable nowcasts across both normal and extreme rainfall regimes. The code is publicly available at wwt.
https:
INTRODUCTION
Rainfall is essential for life and ecosystems, but excessive or intense precipitation often lead to severe natural disasters such floods landslides. With climate change, extreme rainfall events becoming more frequent and intense, further underscoring the importance of accurate precipitation forecasting. To anticipate such events and prepare following risk, precipitation forecasting has been a central subject of meteorological study. While a variety of numerical weather prediction (NWP) models have been developed, it still faces limitations: the high computational cost that makes real-time forecasting challenging, limited spatial resolution that restricts the ability to capture local events, physical parameterization imperfect to represent complex earth system.
"Equal contribution authors. "orresponning author.

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Published as a conference paper at ICLR 2026
Motivated by advances of deep learning in various scientific domains. recent studies have aimed to develop a data-driven model that directly learns spatiotemporal patterns from observations. In particular, for precipitation forecasting, radar-based forecasting model have been proposed as it provides high-resolution and real-time data. Those models have demonstrated performance on benchmark datasets such as SEVIR MeteoNet. However, SEVIR is heavily biased to storm events, while MeteoNet consists of mostly normal rains. Consequently, it remains difficult to evaluate generalization of model both ordinary precipitation throughout year and rare extreme events.
In this work, we introduce a deterministic system for radar-based local rainfall nowcasting with focus on extreme precipitation events, and new dataset that contains both normal and heavy rains, Specifically, we adopt a Video Swin Transformer(Liu et al. 2022) to focus on local patterns, with Cubic Dual-Upae Upssmplee (CDU) decoder to enhance texture fidelity. addition, the decoder upscales temporal dimension to capture more diverse dynamics. Following Temporal Extractor (TE) compress those features to adjust forecasting time horizon without significantly increasing computational cost.
We design the overall architecture to address properties of weather forecasting distinct from usual video prediction. Fisrt, since rainfalls are determined by local meteorological phenomena, we restrict attention blocks using shifted window so that the feature learns local patterns rather than global relation. In addition, we introduce CDU that replaces the traditional upsampling to preserve high- frequency features. Although linear or pixel-shuffle upsampling are known to be efficient for various computer vision task, we observed that linear upsampling strongly smooths out the prediction and removes small area with higher intensity, where could be regarded noise in video prediction. On the other hand, pixel shuffling preserved high-frequency feature in radar but generate unrealistic artifacts. We therefore introduce CDU block that fuses trilinear interpolation and 3D pixel-shufting to remove the artifacts while not compromising the details, thereby enabling to predict extreme events on small regime. Lastly, since CDU expands temporal dimension to learn flexible dynamics while address spatial details or radar data, TE adjust the time steps between outputs and extract dynamics.
To assess performance across wide spectrum of rainfall events, we collect radar data from Korea Meteorological Administration (KMA). Owing to the unique meteorological characteristics of South Korea-where seasonal monsoons and typhoons bring intense rainfall in summer, while lighter precipitation occurs regularly in other seasons-the dataset provides a balanced distribution of rainfall intensities. We evaluate our model leveraging KMA, SEVIR, and MeteoNet dataset, and achieve state-of-the thettheartttt performance.
Our main contributions are summarized as follow:
We propose exPreCast, a deterministic radar-based forecasting architecture that achieves state-of-the-ar accuracy across KMA, SEVIR, and MeteoNet, while maintaining substan- tially lower computational cost.
We introduce the CDU block, which fuses trilinear interpolation with 3D pixel shuffle mitigate checkerboard artifacts and retain small-scale high-intensity rainfall patterns. CDU consistently improves CSI scores, especially with pooling, demonstrating enhanced local-pattern fidelity.
We construct new large-scale dataset encompassing both ordinary and extreme precipitation events, offering more balanced meteorological coverage than existing benchmarks and enabling rigorous evaluation of generalization.
2 RELATED WORKS
Spatiotemporal processing Weather forecasting can be formulated as spatiotemporal sequence prediction, using past observation to forecast outputs in future. (hhi et al. (2015) combined recurrent and convolutional layers in the ConvLSTV to capture spatiotemporal correlations. Based on Fourier Neural Operator (FNO) proposed by Li et al., Pathak et al. (2022) introduced Adaptive Fourier Neural Operator (AFNO) and Nour large-scale weather forecasting system. In parallel, computer vision research advanced transformer-
architectures for sequential image; Liu et al.) (2022) presented the Video Swin Transformer, which leverages hierarchical shifted-window efficiently model local and global context in a video sequence. We adopt this as the backbone and extend with dopain-specific modules tailored to radar-based rainfall nowcasting.
window attention to
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Radar based nowcasting Beyond generic spatiotemporal architectures, number of models have been developed specifically for radar-based precipitation nowcasting. For instance, Gao et al. (2022b) proposed EarthFormer, space-time attention to learn local- socal-global information. More recently, Gao et al. (2023) and Gong et al. (2024) have used a diffusion-basee -hased approach to predict fine features and leverage ensemble methods. Although they have shown state-of-the-ar (SOTA) performance, the diffusion models require much higher computational costs than deterministic counterparts. Lin ct al. 2025) separate networks that learn phase and amplitude, then combine those features in mixer module. It outperforms other baselines such as Earthformer, NowcastNet, and DiffCast on various benchmarks, including SEVIR and MeteoNet, but the datasets are downscaled to greater than 3 times due to computational limitations. In contrast, we develop deterministic model that achieves SOTA results on SEVIR, MeteoNet, and KMA datasets. Our model requires significantly less computational cost and the performance is evaluated at the original resolution of SEVIR and MeteoNet. Moreover, it demonstrates consistently superior performance in forecasting extreme rainfall events.
Radar datasets The development of deep learning models has followed the large-scale benchmark datasets. Veillette et al.) (2020) suggested the Storm Event Image (SEVIR) dataset, which is one of the most commonly used benchmarks for evaluating radar-based precipitation forecasting models. SEVIR contains sequences collected across the United States and provides radar-based observations such as NEXRAD vertically integrated liquid (VIL), satellite imagery, and lightning. As it collects radar data from severe convective storms, SEVIR is biased to heavy rains but less representative of normal rainfalls. By contrast, MeteoNet introduced in Larvor & Berthomier (2021) focuses on years. While it has vast samples for moderate precipitation,
France and includes precipitation extreme rainfall events are underrepresented, limiting the evaluation models across the full spectrum of intensities. In this regard, introduce the KMA dataset, large-scale collection spanning years from 2014 to 2023 at 10-minute intervals. Owing to the meteorological characteristics of South Korea, it covers balanced distribution of rainfall intensities from normal extremely heavy rains, Comparing results on those datasets, we demonstrate that our model generalizes across rainfall distributions and enables more accurate prediction on extreme weather events.
METHODS
We design our model exPreCasi motivated by the specific perspective of radar-based extreme precipi- tation forecasting. First, short-term precipitation is determined by local meteorological features. In addition, extreme rainfall occurs small region with high intensity, which could be easily smoothed out in image processing. Lastly, forecasting system needs to cover varying time horizons. To ad- dress these challenges, propose transformer- -based architecture, which extends the Video Swin Transformer (Liu et al. (2022) to model spatiotemporal radar sequences. Based on the 3D Swin Transformer, we introduce a domain-specific Cubic Dual Upsample (CDU) block in the model to retain fine-grained features and Temporal Extractor (TE) block to adjust time horizons. Figure illustrates the overall architecture of the exPreCast. More details are presented in Appendix
3.1 ENCODER-DECODER 3D SWIN TRANSFORMER
Since rainfall is governed by local meteorological phenomena, we employ Video Swin Transformer as a backbone to joint spatial and temporal dependencies, focusing on local correlations in windows. The architecture employs feature flow. For an input radar volume, the encoder first partitions the data into non-overlapping 3D patches. These patches are then processed through multiple stages of 3D Swin Transformer blocks, with a Patch Merging block at each stage to downsample the features. Within each stage, self-attentions find local correlations between patches in window, while shifting mechanism promotes global context and maintains computational efficiency, as introduced in Liu et al. (2022)
decoerr structure with skip connections to enable multi-scale
The encoded features are further processed by a bottleneck, two 3D Swin Transformer blocks. before being forwarded to the decoder. The decoder mirrors the encoder's ' structure, using 3D Swin Transformer blocks and CDU blocks to enhance upsampling quality. The CDU blocks fuse interpolation, pixel shuffle, and 3D convolutions. Meanwhile, spatial-temporal feature passing mechanism, implemented as skip connections, transfers features between the encoder and decoder through either adding concatenating operation. Then Patch Expanding block projects the feature volume to the target spatiotemporal resolution and TE block adjusts the temporal dimension to match the desired forecast lead time, providing flexible prediction horizons.
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Boadall Paasingg
rador echos
Patch Partition
Predicted
Future radar
Encoder
Betterack Transformer
Decader
Temporal Extractor
FAGL
loss
Decoder Layer (x33
Cubic Upeample
Camaa
""AA"x""""" "x
(x.N)
Shuffic
Expunding
Convao
3D Swin Transformer Blocks
Trilnoar Interpolation
sTx "X""X5
Figure exPreCast Architecture: The encoder compresses input sequences, while the decoder uses the proposed Cubic Dual Upsample (CDU) blocks to reconstruct high-resolution precipitation patterns. The CDU block combines trilinear interpolation and 3D pixel shuffle to refine details and mitigate artifacts. Additionally, dedicated Temporal Extractor (TE) module enables flexible forecasting across various lead times.
3.2 CUBIC DUAL UPSAMPLE BLOCK
Traditional upsampling methods are insufficient for nowcasting extreme events. As shown Figure 2 standard pixel-shuffle preserves overall structure but loses local patterns and introduces checkerboard artifacts. Conversely, simple interpolation creates aliasing effects, severely degrading image details. To address these limitations and improve the prediction of local precipitation patterns, we introduce the CDU block. Inspired by the dual upsample method from Fan et al. (2022). CDU block fuses trilinear interpolation and 3D) pixel-shuffle to improve reconstruction quality and mitigate artifacts in decoded feature volumes. Figure 2 shows that CDU leads enhanced forecasting performance.
The CDU comprises two parallel branched illustrated in Figure I interpolation branch and pixel- shuffle branch. While both branch first apply channel-mixing 3D convolution across spatiotemporal axis, convolution in the interpolation branch intends to preserve the channel but one in the other branch expand to reconstruct high-frequency details. After activation functions and upsamplings in each block, the final 3D convolution fuse the concatenated results.
Given an input feature tensor Zin |bxxxxxxxx, the CDU block produces an output Zout E Roxe" sch Xwe 5 where the target resolution is determined by scale factor (st, 8hs Sw) such that (6", Jt, w²) (sit, h, w). Specifically, the trilinear branch produces intermediate feature 24s E Raxt" xh" x"" by
2/3 Conv3D (Zin) Zzi Conv3D (e) (TI (a (z()))
E bbxtxxxxxx
E pbxt' xh" xh'xx'xx" x
where TI is trilinear interpolation upsampler, a is PReLU activation and Conv3D denotes channel- mixing 3D convolution. Similarly, the pixel-shufflc branch outputs Zpe E gbxtt x xu" x
ps (onv3D (zin)
E R xxxtxxxxxx (s
(2)
2ps (cn (PS (a (z))))
E Rbxtt xx"x""""""""""" x="x
where PS is pixel-shuffle upsampler. Then, the final convolution computes output 2ont from concatenated those intermediate values:
Zout (on (Ets ① kpa) E bx xh'xx"" X
where ( denotes concatenate operation. This construction effectively alleviates smoothing or artifact issues, yielding coherent and fine-grained precipitation structures. The quantitative comparison forecasting performance between upsampling blocks is reported in the ablation studies. Further discussion of the effect of the CDU is provided in Appendix

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Published as a conference paper at ICLR 2026
Pixel Shuffle Upsampling
Interpolation Upsampling
Cubic Dual Upsampling
Ground Truth
Zoomed in Area
Figure 2: Reconstruction results from different upsampling methods. CDU significantly mitigates checkerboard artifacts and preserves tine-grained radar textures, resulting in better reconstructions.
3.3 TEMPORAL EXTRACTOR BLOCK
In precipitation forecasting, the required time horizon varies: very short-term nowcasting is crucial for immediate warnings, while long-term forecasting is more valuable for preparedness. To enable flexible forecasting horizons, append TE block after decoder. By configuring the TE block, we can tailor our models to different operational needs. Given the decoder output Zeecoder E R BxxxxxxX wwx. xc the TE block transforms Zdecoder of the temporal dimension I into Y = TE (Zdecoder) of the target forecast horizon T", using a spatiotemporal 3D convolution:
Y = (T) (Zdecoder) R BXT XXxWCC
where Conv3D(T) denotes spatiotemporal convolution, which slides over H. W and C' dimensions. This allows the model to adapt to varying lead times with minimal computational training cost, facilitating efficient learning across diverse forecasting settings.
Furthermore, the TE block is designed to be bi-lucrative with the decoder framework to maximize performance across different time prediction tasks. For short-term prediction, small scaling factor in time direction is applied in the CDU decoder part, and the TE block then extracts a minimal but meaningful feature from the decoder, resulting an efficient and accurate short-term prediction. For long-ferm prediction, a larger factor is applied along the temporal axis of the CDU decoder. This allows the decoder to learn rich temporal dynamics with the aid of transformer blocks. The TE block then further extracts temporal information, enabling multiple target predictions. Given that both short-term and long-term prediction tasks rely on the same historical information from the past sequences, the encoder part of the model can be shared. This commonality makes it well-suited for transfer learning approach. We implement this by freezing the encoder's parameters after training on short-term prediction task. The long-term prediction model is then initialized with this pre-trained encoder, and only the decoder and the TE block are fine-tuned. This provides a highly efficient training framework, reducing the computational cost of developing long-ten term forecasting models.
4 EXPERIMENTS
To rigorously assess the generalization capability of exPreCast, we evaluate on three distinct datasets: SEVIR, MeteoNet, and KMA. Each dataset reflects different rainfall distribution, providing complementary perspectives on real-world forecasting scenarios. SEVIR predominantly records extreme rainfall (storm) events, MeteoNet is largely composed of moderate precipitation, while KMA exhibits a more balanced distribution spanning normal to extreme cases. This diversity enables comprehensive evaluation of the model's 's robustness across varying rainfall regimes. Detailed deseriptions and comparative distributions of these datasets are provided in the Appendix A
For each dataset, benchmark exPreCast against several baselines, including state-of-the-art the-art methods, under a fair comparison setting. All baselines are implemented following, as closely as possible, the default configurations reported in their original papers. Our model is trained with the
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AdamW optimizer, combined with warm-up scheduler (Loshchilov & Hutter 2019). and optimized using the Fourier Amplitude Correlation Loss (Yan et al. (2024). A full description of the training setup and implementation details is provided in the Appendix C
To assess model performance, we adopt multiple forecasting skill metrics. We primarily use the Critical Success Index (CST), including CSI-p across thresholds p and averaged CSI-M, evaluated both with and without pooling. CSI with pooling is particularly crucial, as it assesses the model's ability to capture local precipitation patterns-the higher the score, the sharper and more coherent predictions. In addition, following Gao et al. (2022b); Gong et al. (202), we report the Heidke Skill Score (HSS), which evaluates categorical forecast accuracy relative to random chance. The definitions of these metrics can be found in Appendix B We also compare efficiency by the number of parameters and inference cost in GFL.Oss, providing insights into computational requirements and reproducibility. Furthermore, we provide qualitative comparison with selective baselines in following subsections and Appendix[F] [F] for the full set of visualizations. Further investigation including adoption of FACL on baselines and other evaluation metries can be found in Appendices E. and E.2 respectively.
4.1 KMA
The Korea Meteorological Administration (KMA) officially provides their product to the public via their API hub and briefly explains their product in the documen[] Among the products from KMA, we use Hybrid Surface Rainfall (HSR) composite radar images, which are generated a 5-minute interval and cover 1152km x 1440km with a spatial resolution of 500m. To construct the KMA dataset in this study, we collect all images from 2014 to 2023 with a 10-minute interval, except some timestamps when no data were provided by KMA, and downscale the spatial resolution to 4km through uniform subsampling. The downscaling process is clearly illustrated in the Appendix A
A key distinction of this dataset is its more balanced distribution of events, from normal to extreme, in contrast to the SEVIR and MeteoNet datasets. This characteristic crucial for evaluating each model's generalization capability. Models are trained to predict the future 6 frames (one hour) from the past 7 frames (one hour). Performance is evaluated after converts predicted radar image via Z-R relation, with thresholds of [1, 4, 8, 10, 20, 40, 80] in mm/h unit.
Table 1: Comparison on KMA with CSI thresholds 20 and 80 across pooling size of 4 and 16
KMA
Parumeters(M)
Flope(G)
CSLM
CSL-20
CSI-80 1
HSS
Model
POOLI
POOL4
POOLI6
POOLI
POOL4
POOLI6
POOLI
POOL4
POOLI6
UNel
19.3
28
0. 1822
0.1674
0.1747
0.0520
0.0633
0.0813
0.0016
0.0066
0,0213
0.2680
Comd.TMM
16.6
7
0.2419
0.2203
0.2203
0.1234
0.1149
0.1242
0.0140
0.1955
0,1237
0.3532
PhyDNer
3.09
161
0.2462
0.2197
0.2332
0.1138
0.1066
0.1218
0.0120
0.1600
0.0196
0.3545
VP
42
0.2762
0.2558
0.1536
0,1612
0.0314
00381
0.395%
FarthFomer
15.1
0.2518
0.2283
0.2302
0.1270
0.1146
0,1256
0.0084
0.0199
0.166
0.3625
APNO
61.7
63
0.1427
0.1268
0.1082
0.0258
0.0283
0.0287
0.0010
0.0025
0.0083
0.2175
CasCled
391.0
1,739
0,2573
0.3742
0,4837
0.1702
0.3066
0.4444
0.8346
0.0926
0.1695
0.3806
AlphaPie
688
0.2530
0.1210
0,1299
0.0234
0.3533
exPreCast
32.0
55
0.2794
0.3721
0.4841
0.1786
0.3013
0.4490
0.0348
0.0835
0.1488
0.4042
Ground Truth
SimVP
Carthformer
AlphaPre
CasCast
exPreCast
mm/hr
12023-07-44 03:40)
80
40
20
10
Zoomed-in Area
0.1
Figure 3: Visualization of I --hour predictions from different models on the KMA dataset.
Table I presents a comparative analysis of forecasting models on the KMA dataset, evaluating their performance via CSI-M, CSI-20. and CSI-80 across multiple pooling levels. Our model demonstrates
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superior performance to the majority of baselines and achieves accuracy comparable to the state- of-the-art but computationally intensive CasCast model. A notable finding is that while CasCast slightly outperforms ours in CSI-80 with pooling, exPreCast requires significantly less computational overhead, with over 30 times lower inference GFLOPs and 10 times fewer parameters. On the other hand, our model achieves the highest HSS score this balanced dataset, which demonstrates its superior categorical forecasting skills and reliable performance under real-world conditions. These results highlight our model's compelling balance of high predictive accuracy and computational efficiency, positioning it as more practical solution.
To further evaluate prediction quality, we provide visual comparison of the last prediction output from the top five models ranked by their CSI-M scores presented in [able As shown in Figure 3 SimVP, Farthformer, and AlphaPre exhibit blurry predictions and are unable identify heavy rainfall events. CasCast, while preserving relatively more detail than the last three models, still fails to precisely detect some heavy rainfall regions. Our model, however, produces the sharpest predictions and accurately identifies the majority of rainfall events.
4.2 SEVIR
The Storm Event Imagery (SEVIR) dataset (Veillette et al. 2020) is a commonly used benchmark for evaluating based precipitation forecasting models. The dataset specifically curated to provide radar observations of extreme rainfall events, capturing storm events over 384 x 384 km region with images at 5-minute intervals for up to 4-hours. Following prior works, we train models to predict the next 12 frames given the past 13 frames. The thresholds for this dataset are based on Vertically Integrated Liquid (VIL), which correspond to pixel values of [16, 74, 133, 160, 181, 219].
Table 2: Comparison on SEVIR with CSI thresholds 181 and 191 across pooling size of 4 and 16
SEVIR
Parameters(M)
Flops(G)
CSI-M
CSL-181 1
CSI-2191 ?
HIS
Model
POOLI
POOL4
PODLI6
POOLI
POOL4
POOL16
POOLI
POOL4
POOL16
UNet
0.3830
0.0853
STT
16.6
33
0.4302
0,4163
0.4475
0.2453
0.2525
0.2977
0.1322
0.1380
0.1734
0,5232
PhyDNet
13.7
70L
0.4198
0.4226
0.4410
0.2526
0.2532
0.2782
0.1362
0.1359
0.1526
0,5311
VP
2.3
132
0.1153
0.1226
0.1530
0.2532
0.260-1
0,3000
0,1338
0.1394
0.1685
0.5280
BarthFomerr
0.4319
0,2562
0.1448
0.1409
0.1481
AFNO
63.3
142
0,3613
0.3666
0,3692
0.1558
0.1539
0.1535
0.0859
0.0916
0.1007
0,4706
CasCast
392.9
1.567
0.4401
0.4640
0.5525
0.2879
0.3179
0.3900
0.1851
0.2127
0,2841
0.5602
AlphaPrr
32.4
5,437
0,3995
0,4100
0,4180
0.3175
0.2315
0.2378
0. 0,1088
0.1330
0.1459
0,4996
exPreCast
32.0
208
0.4179
0,4527
0.4104
0.1859
Ground Truth
SimVP
Earthformer
AlphaPre
CasCast
exPreCast
255
181
133
Zoomed-in Area
74
Figure 4: Visualization of 1-hour predictions from different models on the SEVIR dataset.
Table presents the performance comparison on the SEVIR dataset. Our model demonstrates the best overall performance, particularly for CSI-M with pooling and 16, suggesting its effectiveness in preserving precipitation structures. For extreme events (thresholds 181 and 219), exPreCast achieves the highest scores with pooling 4 and 16, a result only exceeded by the CasCast model. On the other hand, our model also shows high HSS score, demonstrating robust categorical forecasting skills across all precipitation events, even on this dataset curated for extreme rainfall. Notably, the CasCast model requires significantly more computational resourees due to its large number of parameters and GFLOPs. In contrast, our model offers a compelling balance of high prediction accuracy and computational efficiency, making it more practical choice. Figure provides visual comparison of
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the prediction quality the last frames for different models. Proposed exPreCast once again stands out with its ability to produce sharper predictions and better preserve the structural rainfall patterns.
4.3 METEONET
Collected by the French national meteorological service, MeteoNet includes precipitation radar measured every 5 minutes. We use southeastern region and select a top left corner 416 x 416 km. Models predict the future 12 frames from the most recent 12 observations. The thresholds evaluated on this dataset are 19, 28, 35, 40, and 47 (dBZ), with 40 and 47 corresponding to the two most extreme rainfall events.
Table 3: Comparison on MeteoNet with CSI thresholds 40 and 47 across pooling size of 4 and 16
MeteoNet
Pamameters(M)
Hlopz(G))
CSI-M
CSI-401 1
CSI-47
HSS
Model
POOLI
POOL4
POOLI6
POOLI
POOL4
POOLI6
POOLI
POOL4
POOLI6
UNet
77
0.2354
0.3168
0.077%
0.0351
0.382
0.3414
STM
40
0.2911
0.1642
0.1646
0.10%
0.4127
DNet
3.09
812
0.2786
0.2556
0.2279
0.1228
0.1114
0.1179
0.0725
0.6622
0,0717
0.4018
SimVP
2.3
155
0.2941
0.27855
0.2544
D.1450
0.1387
0,1500
0.8001
00806
0.0906
0.3991
FurthFormer
309
0.2723
0.3440
0.2155
0.1186
0.1011
0.1026
0.0561
00481
0.0472
0.3748
AFNO
63.6
166
0.2301
0.2251
0.1818
0.0817
0.1160
0.1125
0.008
00382
0.0372
0.3176
AlphaPre
32.4
6,381
0.2648
0.2421
0.2157
D.1 0.1107
0.1126
0.1197
0.0425
0.4400
0,0490
0.3704
exPreCast
32.0
199
0,2861
0.3452
0.4446
0,1500
0.2145
0.3584
0.0856
0.1393
0.2525
0,4116
Ground Truth
ConVLSTM
SimVP
Earthformer
AlphaPre
exPreCast
dBZ
40
24
Zoomed-in Area
16
Figure 5: Visualization of last-frame predictions from different models on the MeteoNet dataset.
J the comparative performance on the MeteoNet dataset. Our exPreCast achieves superior predictive accuracy, particularly with CSI-M at pooling 4 and 16, and demonstrates its capability to preserve fine-grained precipitation patterns even under conditions dominated by normal rainfall events. We have opted to exclude CasCast from this experiment due to its highly unstable results. Even after a fair training setup led to convergence, the CasCast model predictions were consistently noisy and inaccurate (refer to Appendix C.. The visual analysis in Figure 5 reveals the superior quality of our model, which not only produces sharper predictions but also effectively preserves the structural integrity of the rainfall patterns.
4.4 LONG TIME PREDICTION
Another notable advantage of exPreCast is its flexibility for multi-step-ahead forecasting, which is due TE block and decoding framework. Beyond the 1-hour (short-term) predictions discussed previously, we also conduct 6-hour (long-term) forecasting experiments on the K.M dataset, which consists of 36 future frames. We achieves this by upsampling the CDU decoder output with scale factor of 2 along temporal dimension, then using the TE block to extract the targeted 36 frames.
We trained our model through two distinct approaches: training from scratch, and a transfer learning method where we initialized the parameters from short-term model and then fine-tune only the decoder and TE block while keeping the encoder frozen. We compare our results against other baselines with multi-step-ahead forecasting capabilities, including ConvLSTM, Sim and AlphaPre. For fair comparison, we follow their implementation illustrated respective paper, adapting the output 36 frames based on 7 past input frames the KMA dataset. The results in Table(4repreeen that ex PreCasts attain the best performance against baseline models, and achieves the highest CSI scores with pooling, demonstrating the ability local pattern preserving long-term prediction.
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As shown in Figure our model's predictions not only preserve detailed local patterns but are also the only ones capable capturing heavy rainfall events.
Table 4: Comparison result to 6 hour predictions on KMA. AlphaPre" is trained using half-scaled dataset due to computational limit. exPreCast presents ours finetuned model.
KM
Parameters(M)
Flopo(G)
CSI-M
CSI-201
CSI-80
POOL16
POOL.16
POOL1
Con-LSTM
16.6
42
0.0748
0.0623
0.0572
0.013
0.0137
0.0157
0.0013
0.0021
0.0026
0.1243
SimVp
3,5
0.0327
0.0260
0.0246
0.0066
0.0065
0.0081
0.0014
0.0023
0.0032
0.0595
AlphaPro"
287.2
0.0464
0.0003
0.0004
exPreCast
33.6
229
0.0879
0.1271
0.2262
0.0323
0.0646
0.1542
0.0042
0.0130
0.4155
0.1486
exPreCust
25,5
0.1214
0.1572
0.2289
0.0510
0,0931
0.1708
0.0116
0.0282
0.0642
0.1987
ConVLSTM
AlphaPre
exPreCast [Rrwtuni)
Zoomed-in Area (6-hour lead-tinei
mm/hr
80
40
20
10
0.1
Figure 6: Visualization of 6-hour last-frame predictions from different models on the KMA dataset.
4.5 INVESTIGATION ON CDU AND TE
We conduct a targeted ablation study to investigate the performance contribution of the key architec- tural components: the CDU decoder block and the TE modules.
Our first objective is to rigorously assess the effect of various upsampling strategies. To this end, compare the proposed CDU block not only with the standard Pixel-Shuffle (PS) and Trilinear Interpolation (TI) methods, but also with other common interpolation techniques, including Bicubic (BIC) and Area (AREA) interpolation. within the decoder framework. Jable(rreport.. the CSI scores, including those at the final predicted frame, across different thresholds and pooling levels. The results show that CDU achieves the best overall improvement in CSI scores while better preserving local precipitation patterns, as reflected in the pooling metrics. Furthermore, the CSI scores at the final frame reveal that other methods are unable to deliver reliable performance in the long-term prediction, whereas CDU consistently maintains robustness and delivers superior accuracy across all scenarios.
Table 5: Performance comparison of upsampling methods between pixel shuffle (PS), trilinear interpolation (TT) and cubic dual upsample (CDU) across different pooling levels and CSI thresholds, based on 1-hour predictions from the KMA dataset.
Upsampling
CSI-M
CSI-50 1
CSL-M (1h)
CSI-80
Method
POOL1
POOL4
POOL16
POOLI
POOL4
POOL.1
POOL1
POOL4
POOL.6
POOLII
POOLA
POOLI6
PS
0.2781
0.3577
0.4632
0.0372
0.0797
0.1379
0.1936
0.2577
0.3633
0.0075
0.0287
0.0771
0.2635
0.3564
0.4740
0.0295
0.0764
0.1436
0.1755
0.2530
0,3884
0.0076
0.1023
BIC
0.2629
0.3616
0.4814
0.0307
0.0830
0.1530
0.1785
0.2599
0.3937
0.0098
0.0427
0.1156
AREA 0.2631
0.3584
0.4765
0.0305
0.0773
0.1466
0.1753
0.2531
0.3879
0.0071
0.0345
0.1053
CDU
0.2794
0.3721
0.4841
0.0348
0.0835
0.1488
0.1963
0.2772
0.4053
0.0116
0.0454
0.1197
On the other hand, to assess the TE block's role in balancing short-term detail preservation and long- term dynamic stability, we conduct experiments by fine-tuning pretrained model for short-term prediction (I hour) to various extended lead times, including 2h, 3h, 4h, 5h, and 6h, on KMA dataset. We denote the fine-tuned models using the format "TE-Nf". where N represents the total number of predicted frames (c.g., "TE- 12f" indicates a 2-hour prediction model, given a 10-minute frame interval). The CSI scores for these models across the six different prediction horizons are shown in

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Table 6 The results demonstrate that by extending the predicted time horizon through the TE block, the model successfully achieves reliable and consistent accuracy across different lead times, without sacrificing the high accuracy achieved the short-term horizon. This verifies the flexibilitty and effectiveness of the TE block in adapting the model for various prediction lengths while maintaining forecast quality.
Table 6: Performance comparison of TE modules for different predicted time horizons.
CSI-M (pool16)
1h
2h
3h
4h
5h
6h
TE-6f
0.4053
TE-12f
0.3770
0.2570
TE-18f
0.3854
0.2585
0.1984
TF-24f
0.3508
0.2302
0.1745
0.1408
TE-30f
0.3840
0.2783
0.2161
0.1752
0.1477
TE-36f
0.3923
0.2510
0.1773
0.1347
0.1075
0.0923
DISCUSSION AND LIMITATIONS
Proving an accurate and efficient precipitation nowcasting model is crucial for human safety, as extreme rainfall events have occurred more frequently over the past decade. However, current AI models often struggle balance accuracy and efficiency. Existing deterministic models provide insufficient accuracy on extreme events, while generative-mode soaiiieeemoe--eeeee based ensemble approaches, though highly accurate, require vast computational resources, making them impractical. In this regard, exPreCast would offer a compelling solution by providing high accuracy across diverse rainfall events while maintaining significantly lower computational costs.
The datasets used in this study-KMA, SEVIR, and MeteoNet-differ significantly in scope and characteristics. The KMA dataset is notably extensive, spanning over 10 years and providing a balanced representation of all precipitation scenarios across all seasons. This makes it highly generalized dataset for evaluating a model's robustness and generalization capability under diverse conditions. In contrast, the SEVIR and MeteoNet datasets more narrowly focused, primarily concentrating on either intense rainfall or normal events.
While our model offers flexibility across various prediction horizons, achieving strong performance for long-term extreme rainfall events remains challenging. This could stem from both the scarcity of extreme events and fundamentally ill-posed nature of forecasting that relies on local radar observations. We expect that promising direction is to develop reliable and accurate extrapolation or interpolation to compensate data scarcity, remaining a future work.
Moreover, as a deterministic method, exPreCast produces stable and efficient forecasts but cannot quantiffy predictive uncertainty. Incorporating uncertainty ciimation-potentialll through hybrid approaches that integrate probabilistic methods-would further enhance its applicability, particularly in early warning systems.
CONCLUSION
We introduced exPreCast, deterministic radar-based precipitation nowcasting model designed to capture fine-grained local rainfall patterns. By combining a cubic dual-upsample decoder with lightweight temporal extractor, we achieves state-of-the-art accuracy across diverse datasets. These results demonstrate model's 's superiority and generalizability under different meteorological conditions. Furthermore, ex PreCCsst requires significantly lower computational effort compared to diffusion-baan based models, implying its potential to serve as a practical complement to real-world forecasting systems.
ACKNOWLEDGMENTS
This research was supported by the ASTRA Project through the National Research Foundation(NRF) funded by the Ministry of Science and ICT (No. RS-2024-00440063..
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REFERENCES
Chi-Mao Fan, Tsung-Jung Liu, and Kuan-Hsien Liu. SUNet: Swin transformer unet for image denoising. In 2022 IEEE International Symposium on Circuits and Systems (ISCAS), pp. 2333- 2337. IEEE, 2022.
Zhangyang Gao, Cheng Tan, Lirong Wu, and Stan Z Simvp: Simpler yet better video prediction. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp- 3170-3180, 2022a.
Zhihan Gao, Xingjian Shi, Hao Wang, Yi Zhu, Yuyang Bernie Wang, Mu Li, and Dit Yeung. Earthformer: Exploring space-time transformers for earth system forecasting. Advances in Neural Information Processing Systems, 35:25390-25444 2022b.
Zhihan Gao, Xingjian Shi, Boran Han, Hao Wang, Xiaoyong Jin, Danielle Maddix, Yi Zhu, Mu Li, and Yuyang Bernie Wang. Prediff: Precipitation nowcasting with latent diffusion models. Advances in Neural Information Processing Systemx, 36.78621-78656, 2023.
Junchao Gong, Lei Bai, Peng Ye, Wanghan Xu, Na Liu, Jianhua Dai, Xiaokang Yang, and Wanli Ouyang, Cascast: Skillful high-resolution precipitation nowcasting via cascaded modelling. arXiv preprint arXiv:2402. 440.:2000.. 2024.
Vincent Le Guen and Nicolas Thome. Disentangling physical dynamics from unknown factors for unsupervised video prediction. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 11474-11484, 2020.
Gwennaëlle Larvor and Lea Berthomier. Meteonet: An open reference weather dataset for ai by météo-france. In American Meteorological Society Meeting Abstracts, volume 101, pp. 1-ii, 2021.
Zongyi Li, Nikola Borislavov Kovachki. Kamyar Azizzadenesheli, Kaushik Bhattacharya, Andrew Stuart, Anima Anandkumar, et al. Fourier neural operator for parametric partial differential equations. In International Conference Learning Representations.
Kenghong Lin, Baoquan Zhang, Demin Yu, Wenzhi Feng, Shidong Chen, Feifan Gao, Xutao Li, and Yunming Ye. Alphapre: Amplitudephaase disentanglement model for precipitation nowcasting. In Proceedings of the Computer Vision and Pattern Recognition Conference, pp. 17841-17850, 2025.
Ze Liu, Jia Ning, Yue Cao, Yixuan Wei. Zheng Zhang, Stephen Lin, and Han Hu. Video swin trans- former. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 3202-3211, 2022.
Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. In International Confer- ence on Learning Representations, 2019. URL htttps:://ppereeeetes hetp:/iwwwwweeeeeeeeeeeeeeeeeeee fortt//fummmmm iid=
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Jaideep Pathak, Shashank Subramanian, Peter Harrington, Sanjeev Raja, Ashesh Chattopadhyay, Morteza Mardani, Thorsten Kurth, David Hall, Zongyi Li, Kamyar Arizzadenesheli, et al. Foureast- net: A global data-driven high-resolution weather model using adaptive fourier neural operators. arXiv preprint arXiv:2202.11211 2022.
Suman Ravuri, Karel Lenc, Matthew Willson, Dmitry Kangin, Remi Lam, Piotr Mirowski, Megan Fitzsimons, Maria Athanassiadou, Sheleem Kashem, Sam Madge, et al. Skilful precipitation nowcasting using deep generative models of radar. Nature, 597(7878)662-677, 2021.
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Mark Veillette, Siddharth Samsi, and Chris Mattioli. Sevir: A storm event imagery dataset for deep learning applications in radar and satellite meteorology. Advances Neural Information Processing Systems, 11222009-220111 2020.
Chiu-Wai Yan, Shi Quan Foo, Van Hoan Trinh, Yan Yeung, Ka-Hing Wong, and Wai-Kin Wong. Fourier amplitude and correlation loss: Beyond using 12 loss for skillful precipitation nowcasting. Advances in Neural Information Processing Systems, 37:100007-10004,, 2024.
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A DATASET DETAILS
A.1 KMA DATASET
The Korea Meteorological Administration (KMA) dataset provides nationwide radar coverage for South Korea. It consists of various types of radar reflectivity, such Plan- Position Indicator, Constant- Altitutde PPI, and Hybrid Surface Rainfall (HSR). We used HSR observations obtained every 10 minutes over the central region of South Korea, ranging from longitude 120.5 E to 137.5 E and latitude 29.0" N to 42.0" N. We normalize each image by clipping negative values to O and dividing by 10.000, which results in data values in the range [O, (0.0ldBZ). To reduce computational costs while maintaining nationwide coverage of South Korea, we downsampled the data to a 4 km resolution. We then cropped central 1024 1024 km square, yielding 256 x 256 input image. The KMA radar dataset spans from 2014 to 2023, with data collected every 10 minutes. For our experiments, we used a time-split approach: data from 2014 to 2021 was used for training, 2022 for validation, and 2023 for testing. The model takes a sequence of 7 frames (one hour of past observations) as input.
configured to predict either the future 1-hour short-term horizon (6 frames) the 6-hour long-temn song-temmmae horizon (36 frames), with temporal resolution of 10 minutes per frame.
To evaluate model performance, first converted the radar reflectivity from dBZ to mm/h using the Marshall-Palmer Z-R relationship, Z = 200 as illustrated in KMA's document httpss //datawiki. kma. go. kr We then report the CSI-p for thresholds p [1,4, 8, 10, 20, 40, 80]. Additionally, we applied a mask to exclude pixels outside the radar range, the coverage of which is depicted in Figure
Radar Coverage South Korea
36'E
33'E-
28g
125'E
128"E
131'E
Figure 7: Radar coverage area South Korea meteorological observatorics.
A.2 SEVIR DATASET
The Storm Event Imagery (SEVIR) dataset (Veillette et al.) 2020) is commonly used benchmark for evaluating radar-based precipitation forecasting models. SEVIR contains meteorological data covering 384 384 km region at 5-minute intervals for up to 4-hours, providing satellite imagery, lightning, and radar-based observations such as NEXRAD vertically integrated liquid (VIL). We used the VIL dataset at 1 km resolution and trained model to predict the next 12 frames given the past 13 frames. In other words, a model takes as input E R x13x384x384 the most recent 1-hour radar observations, and predicts y/ IR. 12x3884x38 corresponding to the next hour. The reported CSI-p scores are based on Vertically Integrated Liquid (VIL), which correspond to pixel values of p = [16, 74, 133, 160, 181, 219]. The data value lies in the range [0, 256).
A.3 METEONET DATASET
MeteoNet is an open meteorological dataset released by Météo-Francee the French national meteo- rological service. It provides clean and ready-to-uss collection of weather data, including radar observations, ground measurements, and satellite imagery. In this work, we use the radar component of the dataset, which spans three years (January 2016 to October 2018) with temporal resolution
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of 5 minutes, The radar data covers two geographical regions the north western and south-eastern quarters France. We focus on the south- region, where each radar frame has a spatial resolution of 0.011 and an image size of 515x784 For our experiments, portion to a size of 416x416. The forecasting setup uses an input sequence of 12 frames and predicts output sequence of 12 frames.
crop the upper-left
The thresholds for categorizing rainfall intensity follow those used in Gong et al. (2024): 0.5, 2, 10, and 30 mm/h. Using the standard Z-R relationship, Rt (Z/ (200)-b,, the corresponding reflectivity thresholds are 19, 28, 35, 40, and 47 dBZ. The data lies in the range [0, 70).
A.4 DATASET DISTRIBUTION AND COMPARISON
Our experiments are conducted on three distinct datasets, each offering unique perspective on real-world rainfall forecasting. The SEVIR dataset predominantly captures extreme rainfall events, while MeteoNet is primarily composed of moderate precipitation. In contrast, the KMM dataset provides a more balanced distribution, encompassing full spectrum of cases from normal to extreme. As illustrated in Figure comparison of the dataset distributions reveals that SEVIR is heavily skewed toward extreme rainfall events and MeteoNet toward moderate precipitation. KMA dataset offers more balanced distribution, encompassing broader spectrum of rainfall events. In addition, Table offers a direct quantitative examination for event probability based on KMA's threshold. This characteristic makes the KMA dataset particularly valuable for evaluating model's generalization capability across diverse conditions.
contrast, the
Dataset Distributions
0.4
SEVIR
Probability
0.2
100
Rainfall Intensity (mm/h)
Figure 8: Distribution comparison of three datasets. Here, the precipitation unit (mm/h) for the SEVIR dataset is computed following Robinson et al. (2002).
Table 7: Event probability across KMA's threshold.
Probability
mm/h
mm/h
mm/h
10 mm/h
20 mm/h
40 mm/h
80 mm/h
KMA
0.18
0.15
0.05
0.16
0.17
0.17
0.13
MeteoNet
0.22
0.21
0.09
0.14
0.14
0.09
0.11
Given the inereasing importance of extreme rainfall forecasting, it is crucial to note that extreme heavy rainfall has become more severe over the past decade. As illustrated in Figure 9 temporal analysis of categorized rainfall events on the K.M and MeteoNet datasets reveals notable increase in the frequency of shower events. These events are longer confined to the summer season but are now observed more consistently throughout the year, underscoring critical need for accurate and robust forecasting models.
B EVALUATION METRICS
Following Gao al. 2002b),, we compute CSI-p over all predicted frames. Let 9. y E XTxHXWXC be prediction and ground truth radar reflectivity, where T is the number of output frames, H, W are
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Ligh
Normal
Hewvy
Shower
600
Reiefell
Light
Normal
Herry
Shower
Event (2014)
200
600
620
Reinfell Event (2017)
600
Rainfall Event (2023)
600
Reinfall Event (2018)
402
200
Aug
Sea
OA1
hay
Jan reb
MaT
AgT
May
Aug
Sep
Gct
New
Dec
(a) KMA dataset.
(b) MeteoNet dataset.
Figure 9: Histograms of rainfall events. Light, Normal, Heavy, and Shower events categorized by precipitation thresholds: [1, 4, 10, 40] mm/h on KMA and [0.5, 2, 10, 30] mm/h on MeteoNet.
image sizes, and C' is the output channels. For each threshold we define
H (p. (; = [h,, c [1, h, w. 2 and [t, h, c] 2 p)
(5)
M (p. (; = [h, w. [t, h w. c] < p and y [L, h, w, 2 p)1,
(6)
FA (p, t; v.v) [h, w, [t, h, w, c] 2 and [t, h, w.c < pH]
(7)
CR (p.4; 11h, w, e y [1, h, w, c < p and y [1, h, w.d < p)
In other words, (p, 1), .M (p. (), FA (p, () and CR (p, () denote the number of pixels correspon- dent to hits, misses, false alarms, and correct rejections respectively at lead time t and threshold p. Given the test set nI )n) these four indicators are counted across all samples and time frames as follows:
N T
H(p) = Σ
ΣΣ
H (p, mn. Un),
(9)
T
M(p) = Σ
M (p. t; Un).
(10)
==1
FA(P) Σ
FA Ins Un)
(11)
t=
T CR(p) Σ
Σ
CR 2n
(12)
Then, the CSI-p scores is defined by
H(p)
CSI-p H(p) + M(p) + FA(p)
(13)
addition, the CSI-M score the average over the thresholds set and computed by
CSI-M
Σ
CSI-p.
(14)
P
PEP
CSI is highly sensitive to small spatial misalignment and tends to penalize sharp predictions, over- estimating blurred forecast. To compensate this, CSI computed after pooling suggested in recent studies. For instance, as models produce sharper and more realistic predictions, the gap between CSI with and without pooling increases. In this regard, we measure and report both normal CSI and pooled CSI. Following Ravuri al. (202I), the pooling of CSI scores measure the forecasting skill on local pattern. It applics max saxx-pooiing layer with kernel size of l and stride of k/4 on g. y and then computes CSI scores according to the equation T3 and equation 14
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On the other hand, the HSS score, which evaluates categorical forecast accuracy relative random chance, is computed by
((H(p) x M(D) FA()) x CR(p))
HSS
(H(p) + (FA(() + M(p)) + (H()) CRR)) + M(p)) (15)
P PEp
C
EXPERIMENT DETAILS
This section provides a detailed description of our experimental setups. The baseline models were trained using their official code and configurations, which were adapted to each dataset following the specifications in their original papers. Any adjustments to these default settings are explicitly noted here.
C.I
OURS MODEL TRAINING SETUP
The detailed configuration of our model for 1-hour prediction KMA is summarized in Table For 6-hour prediction on KMA, detail is listed in T Training on SEVIR and MeteoNet follows the same configuration with 1-hour KMA. Additionally, the key components are described as follows.
The input radar volume is first partitioned into non-overlapping 3D patches with patch size of (M, Mr. M..) = (2, 4, 4) along the temporal and spatial dimensions. The encoder consists of three stages with depths [2. 6, 2), meaning each stage includes 2, 6, and 2 3D Swin Transformer blocks, respectively. The decoder mirrors this structure with the same depth configuration [2, 6, 2). and bottleneck composed of 2 additional 3D Swin Transformer blocks is inserted between the encoder and decoder.
For upsampling, we utilize our proposed Cubic Dual Upsample (CDU) blocks, which apply a scaling factor of either (1, 2, 2) for 1-hour prediction or (2, 2, for 6-hour prediction to upsample the spatial-temporal dimensions. At the final layer of the decoder, a Patch Expanding block is employed using a 3D transposed convolution with kernel size and stride set to (M, Ma, Mw) (2, 4, 4) for 1-hour model or 41,4,4 4) for 6-hour model to recover the full spatiotemporal resolution.
To aid the decoder's ' learning, we use temporal feature passing via skip connections, which transfer information from the encoder. The skip connections use either element-wise adding operation for the --hour model or a concatenation operation for the 6-hour model.
Finally, the Temporal Extractor block consists of a 3D convolution, which applies kernel of size (3, 3, 1) with a stride of (1, 1, and padding of (1, 1) along the (H, W, C') dimensions. The spatial kernel size of (3,3) enables the model to leverage neighboring spatial context. We note that the TE block extracts temporal features from the decoder's output to predict the target 6 frames for the 1-hour model, and 60 temporal features for the 36 frames of the 6-hour model.
The optimizer we used for model training is the Adam optimizer with a base learning rate of I 1x 10-3, warm-up cosine learning rate scheduler is applied, with warm-up ratio of 0.2.
We adopted the FACL loss function to train our model. Denote E [0, 1] and let P(t) be a threshold that decreases from to 0 during training. Unlike the stochastic method proposed by Yan et al. (2024). which randomly selects between FAL and FCL based on the condition this approach leads to unstable training the early epochs. Consequently, we employ a more stable training strategy by using a linear combination of the two loss components:
P(t), we observed that
FACL (g. y, t) = (1 P(t)) FAL - Pt)) FCL. FCL.
(16)
The threshold P(t) decreases linearly from 0, so
1- n P (t)
(17)
N
where is the total number of training iterations.
For training the SEVIR dataset, set the total number of epochs 100, consistent with the training setup of other baseline models. Training on other datasets, the maximum number of iterations set to 100,000 with batch size of 16. Model performance is validated every 5,000 iterations using
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the CSI-M score at the last frame prediction, and the model with the highest score is selected for final evaluation.
We equipped with four NVIDIA A6000 GPUs (4 x 48 GB) to train model. Nevertheless, training on 1-hour KMA requires only one A6000 GPU (<= 28GB); training on SEVIR, MeteoNet, and 6-hour KMA require full four A6000 GPUs.
C.2 BASELINE MODELS
The baseline models in this paper-UNeet (Veillette et al., 2020), ConvLSTM (Shi et al., 2013),
PhyDNet (Guen & Thome 2020), Sim (Gao et al., (022a), Earthformer (Gao al. 2022b), AFNO (Pathak et al. 2022). CasCast (Gong et al. (024), and AlphaPre (Lin et al. 2025 were configured following their respective papers, with the exception of CasCast and AlphaPre due to the computational limitation. The CasCast model, which is a diffusion-based approach, leverages deterministic backbones such Si or Earthformer to produce 10-member ensemble for high-resolution nowcasting. Due to its substantial inference cost (exceeding one hundred thousand GFLOPs), reproducing its full ensemble-basee approach was computationally infeasible. To allow for fair comparison, we adapted the CasCast model to generate a single prediction. In the case of the AlphaPre model, its heavy reliance on Fourier Neural Operator structures in Amplitude Network results in significant memory consumption. To relief the computational barrier, we reduced the spatial dimensions (H; W) of the input data by factor of for longer prediction on the KMA.
C.3 TRAINING LOSSES OF CASCAST
Figure 10 represents the training losses of CasCast. The training process consists of 3 steps: training deterministic model (earthformer), training autoencoder, and training diffusion based on the latent data. Although diffusion model shows lower loss in MeteoNet, it might be overfitted to ambiguous latent data given by unstable autoencoder and deterministic model. This may cause weird reconstruction shown in Appendix
8 3.00
5.05
3.30
10-
0.30
Figure 10: Training loss of CasCast on MeteoNet and KMa datasets.
D EMPIRICAL ANALYSIS ON CDU
An empirical analysis is conducted as follows provide an intuitive understanding of how the proposed CDU successfully improves the upsampling performance and mitigates the unrealistic artifacts generated by the Pixel Shuffle or Trilinear Interpolation methods.
The analysis assumes that the target image Y can be decomposed into a smooth low-frequency component You and detailed figh-freyeency (residual) component Yhigh- That is.
= Yoow + Ynigh
First, traditional interpolation methods (c.g., Trilinear Interpolation) generate overly smooth results, where the underlying target image contains highly nonlinear behavior between pixels. Therefore, the TI branch primarily approximates only the low- www-frequncy content of the target image:
TI(X) R Yiow
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Table 8: The details of the exPreCast model on 1-hour KMA dataset. (S)WMSA refers to the (Shifted) Window Multi-Head Self-Attention mechanism, following the design introduced in Liu et al. 2022) In this configuration, non-shifted attention is applied in num-nueee Swin Transformer blocks, while the shifted window mechanism is used in even-numbered blocks.
Block
Layer
Resolution
Channels
Inper
256 256
Embscoling
255
Layersoora
4 125 x 128
3D Swin Transformer Blncks, x22
(S/AXS
x 128
x 128
96
Layertlora
128 128
Patch Merging
Linear
x 128
1228 -+-1 x 04
96 - 192
Layerloom
4 64
102
Layezlozu
AD Swin Transformer Blocks x6
x x 64
192
Layerboora
x 64 x 64
192
ML.P
Parch Merging
Linear
Layerliorn
4 64
4.x 12
384
354
Layerilor
x 32
381
3D Swin Transfrmer Blocks x
Layerloom
x 322
HLP
x x 122
384
Merging
Layerllora
4x
Layerllarn
x 16
768
(8)WVSА
Transformer
Layarllarm
MLP
xx 16 x 16
768
Doav3D
16
708
13336
Pi mal Shuff
4 16
1536
394
Doev3D
4.x -12
384
Upsample
Upsampline ling
4 16
16 -
768
Donv50
x x 32
768 3384
Concatenaate
0oav30
x 32
384
Layerllora
4 32 32
38.0
Feature Passing
SiripConnaction (Adding)
322
384
Layerllon
32
3D Swin Transformer Blocks x
(3/AXSА
4 32 32
58.1
Layertlor
x 32
584
Donv30
4
384 > 7778
PReLU
4 12
708
mel Shuf
Doav30
Dosv30
4.x 32x332 32.x 32
384
Cubicc Dual Upsample
PRa!
4.x x 322
384
Upsanpling
Oony30
1922
Londa Lancat
4xx 64
1922 354
CoavS0
x 64
384
192
Feature Passing
BleppConeectin (Adding)
4 64 64
192
Layerilom
x 64
192
(5)WMS4
Swin Transformer
LayerHlora
x 64
192
MLPP
x 64 x 64
192
Coav30
x 64
192 384
PiaeiShurffee
4 64
128 126
384 - 96
Dowv50
128 x: 128
96
84 644
Upsample
Uppanmp 1
4. 64
128
192
DoavS0
128 x 128
192
Coar30
4 128 x 128
192 + 96
Layerllom
4 128 x 1228
96
Feuture Passing
SkipConnection (Adding)
128 128
Layerllama
128 x
3D Swin Transformer Blocks x2
(8/AYS4
4.x 128 x 1928
966
Layerllor
x 128 x 128
966
Farch Expanding
1rancporedoav30
4: 125 x 128 =
250
-9
Temporal Estractor
Cosv30
x 258 256
258 256
18

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Published as a conference paper at ICLR 2026
Table 9: The details of the exPreCast model 6-hour KMA dataset. (S)WMSA refers the (Shifted) Window Multi-Head Self-Attention mechanism, following the design introduced in Liu et al. (2022).
In this configuration, non-shifted attention applied in odd-numbered Swin Transformer blocks. while the shifted window mechanism is used in even-numbered blocks.
Block
Layer
Resolution
Channels
Input
7 266 256
Parch Paritioo & Linear Finbesdiing
Bonvãn
7x 238
LayorHorn
16
Swin Tranformer Blocks
(S)MNSA
x 128
128
96
LayorNorn
MLP
x 128 128
96
Merging
LayorNorm
x 64
96
192
LayerHorn
x 01 X
192
Swin Transfrmmrr Blocks x6
(8)ANSA
64
192
LayerNorn
x 61 61
192
MLP
64
Patch Merging
Linear
4. 64
1972 3344
LayerNorn
x 32
381
LayerNorn
x
52
384
3D Swin Trunsormer Blocks
(SJMNSA
4 x
384
LayorHorn
ML.
4
x.32
384
Morging
Linear
3232
x
384 788
LayerNorn
4xx 16
16
7G8
Layerlorm
4 16
18
768
Swin Transfrmer Blocks
(S)ANSA
x 16
708
LayerNorn
x 16
16
768
MLP
708
Conv30
4 16
16
768 - 3072
PRall
4 16
3072
Pixza Sburfle00
4 16
16 -
32
3072 -9 384
Conv3D
8 32
384
Cabic Dual Upsample
PRe1l
x 16 16
768
Upaampl
4 16
768
Conv30
x
768
Con temate
82 x2.2
368
768
768
LayerWorn
8 8: x2
384
Teature Passing
Concatenate
384
LayorNorn
12 32
384
Swin Transformer Rlocks
(8JMNB
12 x 32 32
384
LayorHorn
Mt.P
12 x
x 32
384
Conv3U
12
384
L536
PReLU
12 x
1536
12 32 32
24 64 64
1538 is 192
Conv3u
12 32 12
US4
Cubic Dual Upsample
PRcLU
12
384
Upsampl 1ng
32
x:32
Conv30
24 64 x 64
384 -> 192
Don
192
Conv30
24 x
x
384
192
LayerWorm
24 x 64 x 64
192
Passing
Concatbenate
LayerWorn
229 64 x 64
1922
(3)AMSA
LayorNorm
x
192
MLP
28 x
192
Conv3D
192
PRa1l
28 64 x 64
768
PiselSbuff1ee3
Conv30
x 128 x 128
96
Conv30
28
192
Dual Upsample
Upeamplise ling
28
64 64 56 128 128
192
Donv30
56 128 128
Concabenate
x 125 x T288
MR
132
Conv30
56 x 128 x 128
192
96
LayerHorn
128 128
Feature Passing
Consatenate
(56 -4) x 128 x 128
96
Laye:Norn
128
3D Swin Transfomer Blocks x2
Layeriorr
(SJANSÁ
80 x 128 x 128
60 x 128 x 128
96
96
XLP
128
Parch Expanding
TrangponeConv30
60 128 128 60 256 256
96
Temporal Extractor
Conv3D
60
256 -+ 256
19

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Published as a conference paper at ICLR 2026
On the other hand, while the Pixel Shuffle (PS) can capture the high-frequency part, it typically attempts to reconstruct the entire image in one step:
PS(X) R Y Yoow Ynigh
However, it is empirically observed that forcing PS to learn both low- and high-frequence details simultancously often leads to undesirable checkerboard artifacts in the output (as demonstrated in Figure 2). Consequently, our proposed CDU, which fuses the TI and PS branches, leverages residual learning. By allowing the TI branch to capture the dominant Ylow. the task of the PS branch is effectively decoupled. This enables the PS branch to focus primarily on reconstructing the high-frequency residual Ynigh. That is,
PS(X) - (Y -T((X) Ynigh
Therefore, by decoupling the reconstruction task, where the TI branch handles the smooth low- frequency content and the PS branch efficiently targets the high-frequency residuals, the CDU successfully mitigates the unrealistic artifacts while ensuring high structural fidelity in the final prediction.
E
MORE QUANTITATIVE RESULTS
E.1
IMPACT OF FACL ON BASELINE MODELS
The performance superiority of our exPreCast model results from the synergistic integration of the proposed CDU decoder, TE blocks, and the utilized FACL loss function. While FACL contributes to an improvement in predictive texture and forecasting quality, its effective integration within our architecture yields the best overall results. To validate the coupling effect and demonstrate the intrinsic strength of exPreCast's design, conducted experiments where the default loss functions of strong baselines were replaced with FACL. This comparison includes the top three baselines, ranked by their CSI-M in Table T The results, detailed in Table TO show that although FACL indeed provides a general performance boost to these existing models, exPreCast still achieves the highest overall performance. This outcome confirms that the superior performance is primarily attributable to the specific architectural innovations of ex exPreCast.
Table 10: Impact of the FACL loss function on baseline models.
CSI-M
CSL-20
CSI-80
Model
Loss
POOLI
POOL4
POOLI6
POOLI
POOL4
POOLI6
POOLI
POOL4
POOL16
0,2762
0.2558
0,2598
0.1536
0.1444
0.1612
0.0314
0.0381
0.0443
0.3958
SimVP
FACI.
0.2691
0.3548
0.1722
0.3912
Earthformer
0.2518
0.2283
0,2302
0.1270
0.1146
0.1256
0.0084
0.0129
0.0165
0.3625
FACL.
0.1.977
0.2712
0,3762
0.0519
0.1605
0.3121
0.0063
0.0332
0.0948
0.2417
AlphaPre
0.2530
0.2311
0,2330
0.1210
0.14
0.1299
0.0172
0.0204
0.0234
0.3533
FACL
0.2539
0.3334
0.4367
0.1540
0.2549
0.3844
0.0232
0.0586
0.1000
0.3663
exPreCast
FACI.
0.2794
0.3721
0.4841
0.1786
0.3013
0.4490
0.0348
0.0835
0. 1418
0.4042
E.2
OTHER EVALUATION METRICS
Throughout this paper, the CSI scores serve as the primary performance indicator, aligning with established meteorological literature due its robust field standardization and domain- -exairt in- terpretability (c.g., ECMWF Technical Memoranda No. 430, see Nurmi (1994)). Nevertheless, to ensure a comprehensive validation, we also evaluate performance using several other common metrics frequently utilized in nowcasting literature: Mean Absolute Error (MAE), Mean Square Error (MSE), Fractional Skill Scores (FSS), and Regional Histogram Divergence (RHD) (proposed in Yan et al. (2024)). While MAE/MSE assess point-wise accuracy commonly used in image prediction, FSS and RHD offer valuable insights into distribution preservation and structural fidelity. These metrics were evaluated across all trained baselines and model the KMA dataset, with FSS/RHD using neighbor size of 15 and consistent precipitation thresholds. The full comparative results are detailed in Table
20

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Published as a conference paper at ICLR 2026
Table 11: Performance evaluation using various metrics on the KMA dataset.
Model
Parameters(M)
Flops(G)
MAE
MSE
FSS
RHD
UNet
19.3
28
0.0753
10.6032
0.4802
0.0478
ConvLSTM
16.6
7
0,0524
0.5755
0.5419
0,0366
PhyDNet
3.09
161
0.0488
0.5474
0.5264
0.0377
SimVP
1.9
42
0.0482
0,5219
0.5594
0.0325
Earthformer
15.1
61
0.0514
0.5579
0.5542
0.0352
AFNO
61.7
63
0.0622
0.7400
0.4332
0.0632
CasCast
391.0
1,729
0.0958
3.1821
0.6108
0.0178
AlphaPre
8.5
688
0.0491
0.5420
0.5720
0,0363
exPreCast (Ours)
32.0
55
0.0639
3.1342
0.5950
0.0122
The results in Table I further validate exPreCast's superiority, demonstrating its ability to not only achieve high skill (CSI) but also to produce outputs with high structural fidelity (FSS) and accurate intensity distributions (RHD). Although model achieves comparable accuracy to most baselines on point-wise metrics (MAE/MSE), it is crucial to note the inherent limitations: point-wise metrics generally considered insufficient for robust precipitation forecasting evaluation. As widely recognized, MSE tends to smooth out high-intensity regions, leading to performance underestimation extreme events. Therefore, CSI remains the most effective metric for capturing performance across various event intensity levels. Furthermore, we emphasize exPreCast's s efficiency advantage: while competitor like CasCast may show marginally better FSS score. its computational overhead (GFL .OPs) is more than 30 times greater than ours, underscoring the superior utility of our proposed model.
F MORE QUALITATIVE RESULTS
VARIOUS MODELS PREDICTIONS ON KMA DATASET
We present more qualitative results from various model predictions on KMA dataset, shown in Figures
and 13
F.2
VARIOUS MODELS PREDICTIONS ON SEVIR DATASET
We present more qualitative results from various model predictions on SEVIR dataset, shown in
Figures 14
15
and 16
F.3
VARIOUS MODELS PREDICTIONS ON METEONET DATASET
We present more qualitative results from various model predictions on MeteoNet dataset, as shown in
Figures 17
18
and 19
F.4
SIX HOURS PREDICTIONS KMA DATASET
We present here the qualitative results from 6-hour predictions models studied in Section 4.4 Fig- 20 visualizes the prediction every hour.
21

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Published as a conference paper at ICLR 2026
10m
++ 20m
30m
40m
t+ 50m
60m
mmttr
Targets
20
10
exPreCast
UNet
ConvLSTM
PhyDNet
SimVP
Earthformer
AFNO
CasCast
AlphaPre
Figure 11: A set of example forecasts on KMA.
22

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Published as a conference paper at ICLR 2026
10m
c 20m
r+ 30m
40m
++ 50m
60m
minur
Targets
20
10
exPreCast
UNet
ConVLSTM
PhyDNet
Earthformer
AFNO
CasCast
AlphaPre
Figure 12: A set of example forecasts KMA.
23

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Published as a conference paper at ICLR 2026
10m
+ 20m
30m
40m
++ 50m
60m
mmttr
Targets
10
10
exPreCast
UNet
ConvLSTM
PhyDNet
Earthformer
AFNO
CasCast
AlphaPre
Figure 13: A set of example forecasts KMA.
24

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Published as a conference paper at ICLR 2026
Target
exPreCast
UNet
ConVLSTM
PhyONet
SimVP
ForthFormer
AFNO
CasCast
AlphaPre
Figure 14: A set of example forecasts on SEVIR.
25

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Published as a conference paper at ICLR 2026
min
min
Target
8
5
exPreCast
UNet
ConVLSTM
5
PhyDNet
SimVP
Earthformer
AFNO
CasCast
AlphaPre
Figure 15; A set of example forecasts on SEVIR.
26

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Published as a conference paper at ICLR 2026
Target
exPreCast
UNet
ConVLSTM
PhyONet
SimVp
EarthFormer
AFNO
CasCast
AlphaPre
Figure 16: A set of example forecasts on SEVIR.
27

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Published as a conference paper at ICLR 2026
Tr 30m
20m
30m
40m
50m
te 50m
Torget
exPreCast
PhyONet
SimVP
Ear
APNO
AlphaPre
CasCast
Figure 17: A set of example forecasts on MeteoNet.
28

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Published as a conference paper at ICLR 2026
To 10m
20m
30m
40m
50m
50m
Torget
MePheCast
PryONet
Sinve
Ear
AFNO
AlphaPre
CasCast
Figure 18: A set of example forecasts on MeteoNet.
29

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Published as a conference paper at ICLR 2026
Tr 10m
20m
30m
40m
50m
50m
Torget
exPrecass
PhyDMet
Sinve
Carthformer
AFNO
CasCast
Figure 19: A set of example forecasts on MeteoNet.
30

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Published as a conference paper at ICLR 2026
Inputs
40
+ 60m
t+ 120m
180m
t+ 240m
t 300m
t+ 360m
Targets
ConVLSTM
SimVP
AlphaPre
exPreCast
exPreCast (finetune)
Figure 20: A set of example forecasts 6-hour KMA.
31

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Published as a conference paper at ICLR 2026
G BROADER IMPACT
This work focuses on high-resolution precipitation nowcasting, which plays a critical role in antic- ipating localized extreme rainfall events. By enabling more accurate short-term forecasts fine spatial scales, the proposed model has the potential to significantly improve public safety and dis- aster preparedness. In particular, it can support early warning systems that help protect lives and reduce property damage during extreme weather events. Accurate and timely rainfall prediction especially crucial in densely populated regions, where even short delays in response can lead to severe consequences. While the model demonstrates good performance, careful coordination with operational forecasting systems and transparent communication with stakeholders are essential to ensure its effective and responsible deployment.
32


Reviews:
[{"review_id": "qc8OsbM5df", "reviewer": "Reviewer_kH8J", "summary": "This paper introduces exPreCast, a deterministic deep learning framework for extreme weather nowcasting using radar-based precipitation data.\nThe authors aim to address challenges in forecasting localized and fine-scale rainfall patterns, especially under extreme conditions, where existing diffusion-based generative models are accurate but computationally heavy, and deterministic ones are efficient but biased toward normal rainfall. exPreCast builds on a Video Swin Transformer backbone and incorporates two key modules: Cubic Dual Upsampling (CDU) and Temporal Extractor (TE). The authors also construct a new balanced radar dataset, KMA, covering both normal and extreme precipitation events in South Korea (2014–2023). Extensive ablation studies and long-term prediction results further validate the model’s robustness and efficiency.", "questions": "How were the samples in the KMA dataset selected and quality-controlled?\nWhat are the differences in MAE and MSE among the different models?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "Balanced new dataset: The introduction of the KMA dataset provides a valuable contribution for evaluating generalization across both normal and extreme rainfall conditions.\n\nStrong empirical performance: exPreCast achieves SOTA results across multiple benchmarks, demonstrating robustness under diverse meteorological regimes.\n\nComprehensive experimentation: The paper includes rigorous comparisons, ablations, and qualitative visualizations that convincingly support the claims.", "weaknesses": "Lack of Discussion on FACL:\nThe authors employ FACL in their model but provide no related discussion. FACL contributes significantly to texture generation and substantial forecast improvements[1]. For fairness, the authors should present a comparison between other models (e.g., SIMVP) with FACL and the proposed exPreCast, or alternatively, compare the performance of exPreCast trained with MSE loss against other models.\n\nUnfair Comparison Due to FACL in Different Forecast Durations:\nAlthough the authors claim that the CDU decoder can adapt to various forecast lengths, the use of FACL still makes comparisons across different forecast durations unfair. It becomes difficult to distinguish whether the performance gain for long-term forecasting comes from FACL or from the CDU decoder itself.\n\nLack of Discussion on the Distribution of Precipitation Events Across Datasets:\nLines 18–21 state that previous datasets are limited to a single type of precipitation event, whereas KMA balances different precipitation types. However, this claim lacks statistical support. The authors should analyze and compare the distribution of precipitation events at different thresholds across datasets.\n\nIncomplete Ablation Study on Upsampling:\nThe ablation on upsampling is only conducted on exPreCast, which is insufficient to demonstrate that upsampling is a general issue across all deterministic models. Moreover, the range of interpolation methods compared is limited—commonly used methods such as bicubic and area interpolation are missing. Exploring multiple interpolation approaches across different models would yield more valuable insights.\n\nIncomplete Visualization in Figure 6:\nFigure 6 should include all forecast frames from one hour to six hours after the first frame. Providing the complete sequence would allow reviewers to better evaluate the visual quality and temporal consistency of the generated forecasts.\n\nReference:\n[1] Fourier Amplitude and Correlation Loss: Beyond Using L2 Loss for Skillful Precipitation Nowcasting [NIPS2024]", "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": "mDODYD55Wh", "reviewer": "Reviewer_mKKA", "summary": "The paper describes a deterministic method for generating radar forecasts. It also proposes a novel dataset, which is based on the data from Korea Meteorological Administration (KMA) and contains a balanced selection of meteorological events, which is aimed at reducing the bias towards either ordinary or extreme events. The choice of KMA data is due to the wide range of meteorological events: the country has intense rainfall in summer via monsoons and typhoons, and lighter precipitation in other seasons.", "questions": "1. Would it be possible to provide confidence intervals for Tables 1-3?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "Originality: \n- the contributions include both the dataset and the method for precipitation forecasting aimed specifically at enhancing extreme events nowcasting. The main methodological contribution includes the upsampling described in Section 3.2\n\nQuality: \n- Good performance: the experimental results (Table 1-3) show consistently good performance.  \n\nClarity:  \n- the description of the work looks clear and easy to follow, and I believe the description is correct. \n\nSignificance:\n- while the operational and novel deep-learning models cope reasonably well with the ordinary precipitation, the extreme precipitation is an important unsolved problem. \n- availability of the big benchmarking dataset is an important asset for the research in the area, which also contributes towards the significance of this work.", "weaknesses": "Significance and originality:\n- I can see there are two important points which are in advantage for the significance of the paper: (1) proposition of the Cubic Dual Upsample Block (2) dataset. Saying that, however, while the Cubic Dual Upsample Block is justified empirically in the ablation studies, it does not justify why it happens. Perhaps, one could create a link in the appendix, offering the analysis why this might lead to the improvements (if it follows from the existing literature such as Fan et al that would be also totally fine, however the justification needs to be there). This could be a theoretical justification or some additional empirical analysis which would help answer not only whether it helps but also what makes it work better. On point (2), the dataset, I would expect the authors to say whether it would be released (after careful reading I couldn't see it clearly), and put some descriptions about how this dataset is created. Now, I can only see in Appendix A that 'we first converted the radar reflectivity from dBZ to mm/h using the Marshall-Palmer Z-R relationship, Z = 200R1.6. We then report the CSI-p for thresholds p= [1,4,8,10,20,40,80]. Additionally, we applied a mask to exclude pixels outside the radar range' Maybe, comparing these statistics with the statistics of the existing datasets would be useful and would better justify this part of the contribution. \n\nI would expect the authors also to put a list of contributions in the end of the intro, that would help navigate through the methodology.\n\n\nChi-Mao Fan, Tsung-Jung Liu, and Kuan-Hsien Liu (2022). SUNet: Swin transformer unet for image denoising.IEEE International Symposium on Circuits and Systems (ISCAS), pp. 2333–2337. IEEE, 2022", "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": "igjpPhlFfH", "reviewer": "Reviewer_Fimn", "summary": "The proposed model exPreCast is a Transformer-based deterministic precipitation nowcasting model that integrates local spatiotemporal attention, texture-preserving CDU decoder and TE block to flexibly adjust forecast lead times, which also constructs a balanced KMA dataset containing ordinary and extreme precipitation. Experiments on KMA, SEVIR and MeteoNet show exPreCast’s  outperform baselines like ConvLSTM and SimVP.", "questions": "The questions here are related to the three points described as weaknesses.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "1. ExPreCast constructs a balanced KMA dataset containing both ordinary and extreme precipitation, addressing the imbalance of existing datasets, and providing more comprehensive data support for evaluating model generalization. \n2. ExPreCast integrates the CDU decoder and TE block, enabling the model to perform well in both 1-hour short-term and 6-hour long-term forecasts.", "weaknesses": "1. This work lacks a comparison with GAN-based methods, and adding such a comparison can provide a more complete assessment of exPreCast’s performance. \n2. This work would be better to include verification related to the impact of the TE block in the ablation study section to quantify the effect of the TE block on balancing the detail preservation of short-term forecasts and the dynamic stability of long-term forecasts.\n3. Relying on CSI as an indicator may not fully judge the quality of precipitation predictions. The average intensity of precipitation predictions will significantly affect CSI. The authors can consider adding the scores which aims to assess how closely the nowcasting outputs.", "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": "AWbS301bcl", "reviewer": "Reviewer_aEjv", "summary": "This paper proposes exPreCast, an efficient deterministic deep learning framework for short-term precipitation (nowcasting), focusing on extreme rainfall events. exPreCast is built upon the Video Swin Transformer and introduces a Cubic Dual Upsample (CDU) decoder to preserve fine radar texture and a Temporal Extractor (TE) for flexible forecasting horizons and dynamic adjustment. The authors also construct a balanced and extensive radar dataset (from the Korea Meteorological Administration, KMA) to complement existing imbalanced benchmarks like SEVIR and MeteoNet. Experiments across all three datasets show that exPreCast achieves state-of-the-art or comparable performance with far less computational cost, making it feasible for real-time applications. Ablation studies further support the efficacy of the proposed modules, especially the CDU. The paper also discusses limitations and potential future directions.", "questions": "Could the authors present ablation results for the TE, backbone transformer, skip connections etc., and analyze how removing each affects performance and failure patterns?\n\nCan the authors clarify the details of KMA data collection, labeling, preprocessing, and release? What measures ensure its broad representativeness and high physical credibility?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "The paper cleverly integrates efficient CDU and TE modules, significantly improving precipitation nowcasting accuracy and generalizability for both extreme and normal regimes, while maintaining low computational cost—making it highly practical for real-world use.", "weaknesses": "While there is an ablation comparison of upsampling methods, there is no systematic ablation of other core modules—such as the backbone transformer, TE module, or skip connection. The contributions of each component if removed or replaced are not separately quantified, making it hard to attribute performance gains.\n\nAlthough the KMA dataset is introduced as intermediately balanced, details about its collection, labeling standards, preprocessing, and physical data quality are thin. There is also little about public release or ensuring its fairness and community usability.", "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)