AGCD: Agent-Guided Cross-Modal Decoding for Weather Forecasting

Accurate weather forecasting is more than grid-wise regression: it must preserve coherent synoptic structures and physical consistency of meteorological fields, especially under autoregressive rollouts where small one-step errors can amplify into structural bias. Existing physics-priors approaches typically impose global, once-for-all constraints via architectures, regularization, or NWP coupling, offering limited state-adaptive and sample-specific controllability at deployment. To bridge this gap, we propose Agent-Guided Cross-modal Decoding (AGCD), a plug-and-play decoding-time prior-injection paradigm that derives state-conditioned physics-priors from the current multivariate atmosphere and injects them into forecasters in a controllable and reusable way. Specifically, We design a multi-agent meteorological narration pipeline to generate state-conditioned physics-priors, utilizing MLLMs to extract various meteorological elements effectively. To effectively apply the priors, AGCD further introduce cross-modal region interaction decoding that performs region-aware multi-scale tokenization and efficient physics-priors injection to refine visual features without changing the backbone interface. Experiments on WeatherBench demonstrate consistent gains for 6-hour forecasting across two resolutions (5.625 degree and 1.40625 degree) and diverse backbones (generic and weather-specialized), including strictly causal 48-hour autoregressive rollouts that reduce early-stage error accumulation and improve long-horizon stability.

Task-Adaptive Parameter-Efficient Fine-Tuning for Weather Foundation Models

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 will be released.

TianQuan-Climate: A Subseasonal-to-Seasonal Global Weather Model via Incorporate Climatology State

Subseasonal forecasting serves as an important support for Sustainable Development Goals (SDGs), such as climate challenges, agricultural yield and sustainable energy production. However, subseasonal forecasting is a complex task in meteorology due to dissipating initial conditions and delayed external forces. Although AI models are increasingly pushing the boundaries of this forecasting limit, they face two major challenges: error accumulation and Smoothness. To address these two challenges, we propose Climate Furnace Subseasonal-to-Seasonal (TianQuan-Climate), a novel machine learning model designed to provide global daily mean forecasts up to 45 days, covering five upper-air atmospheric variables at 13 pressure levels and two surface variables. Our proposed TianQuan-Climate has two advantages: 1) it utilizes a multi-model prediction strategy to reduce system error impacts in long-term subseasonal forecasts; 2) it incorporates a Content Fusion Module for climatological integration and extends ViT with uncertainty blocks (UD-ViT) to improve generalization by learning from uncertainty. We demonstrate the effectiveness of TianQuan-Climate on benchmarks for weather forecasting and climate projections within the 15 to 45-day range, where TianQuan-Climate outperforms existing numerical and AI methods.