Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2606.02005

Multiscale Dynamics of Heatwave Persistence and Intensity Under Climate Change

Md Tasim Ferdous·Reda Snaiki·Abdelatif Merabtine
View original source ↗Read source document ↗Export metadata ↓

Abstract

Climate change is expected to increase heatwave risk, but exceedance frequency alone cannot explain why some regions show stronger amplification in event persistence. This study develops an integrated event-dynamical workflow to diagnose changes in warm-season heatwaves and link them to coherent, multiscale structures of temperature variability. Heatwaves are identified over southern Canada using a fixed historical 90th percentile threshold (2001-2010 reference, 15-day moving window) and a minimum-duration criterion. Events are summarized using frequency (HWF, HWN), persistence (HWMD, HWD), and intensity (HWI, HWM) metrics. The daily mean temperature field is analyzed using multiresolution dynamic mode decomposition (mrDMD). Event and dynamical perspectives are connected through heatwave-conditioned mode participation ratios and spatial alignment analyses between mode-energy footprints and gridded heatwave metrics using hotspot overlap and Spearman rank association. The workflow is applied to CORDEX-NAM12 regional simulations (CRCM5 downscaling of CanESM5) under SSP5-8.5 for 2016-2025, 2051-2060, and 2091-2100. Results show a clear shift toward persistence-dominated heatwave regimes in the continental interior. By the late century, increases in seasonal heatwave days are accompanied by much longer events, with regional HWMD reaching about 26.66 days/event and HWD about 69 days, together with stronger above-threshold intensity, with HWI reaching about 6.88 K. Dynamical diagnostics indicate a redistribution of dominant activity toward lower-frequency levels and weaker effective damping in interior regions, while coastal and maritime regions show smaller changes. Heatwave-relevant low-frequency modes remain active during long events and align with persistence and intensity hotspots, supporting a process-informed interpretation of regional heatwave amplification under climate change.

Explore related subjects

physics.ao-ph

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Md Tasim Ferdous, Reda Snaiki, Abdelatif Merabtine. 2026-06-01. Multiscale Dynamics of Heatwave Persistence and Intensity Under Climate Change. https://arxiv.org/abs/2606.02005

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

FAST-ML: A Hybrid Physics-Machine Learning Framework for Tropical Cyclone Intensity Forecasting

Rapid intensification (RI) remains one of the most consequential and difficult aspects of tropical cyclone (TC) forecasting. Although full-physics numerical weather prediction models can represent the processes governing RI, resolving storm-environment interactions remains computationally expensive, while purely data-driven approaches often lack physical interpretability. We present FAST-ML, a hybrid framework that bridges data-driven efficiency with physical constraints. A physically informed dual-stream neural parameterization ingests 3D ERA5 fields to diagnose ventilation controls---environmental wind shear and mid-level entropy deficit. By optimizing these parameters end-to-end through a differentiable FAST intensity model, this architecture establishes a robust new paradigm for observation-driven parameter optimization, ensuring storm evolution remains strictly governed by thermodynamic principles. By better capturing the storm's continuous intensity evolution, FAST-ML improves upon its physical baseline, reducing ensemble CRPS across forecast lead times, with a reduction of approximately 31% at 60 h and nearly halving the RI false alarm ratio without sacrificing detection skill. In a 100-member ensemble configuration, FAST-ML produces intensity forecasts comparable to FNV3 for selected storms under the evaluated input configurations. Furthermore, zero-shot tests on selected Eastern Pacific storms provide encouraging evidence of cross-basin transferability. FAST-ML provides a modular intensity forecasting framework that can be coupled with externally supplied storm tracks and environmental fields. It demonstrates that observation-driven parameter learning within physically constrained dynamics simultaneously enhances accuracy, interpretability, and computational efficiency.

physics.ao-ph↗
arXiv2026

West-WRF AI 2-km: High-Resolution Prediction of Integrated Vapor Transport and Precipitation

We introduce a stretched-grid artificial intelligence (AI) weather forecasting model with 2-km resolution over the western United States and part of the Northeast Pacific and approximately 31-km resolution elsewhere globally. Forecasting over the western U.S. is challenging because complex topography and atmospheric rivers (ARs) strongly influence orographic precipitation. West-WRF AI 2-km builds on a global model pretrained with a 40-year European Centre for Medium-Range Weather Forecasts Reanalysis v5 (ERA5) dataset and is fine-tuned with the Center for Western Weather and Water Extremes (CW3E) 2-km regional reanalysis to produce autoregressive 6-hourly forecasts of precipitation and integrated vapor transport (IVT). Forecasts are evaluated over winters 2020-2023 using gridded precipitation observations, rain gauges, and AR Reconnaissance dropsondes and are benchmarked against coarser-resolution AI forecasts and regional and global numerical weather prediction (NWP) systems. West-WRF AI 2-km reproduces observed precipitation-intensity distributions, retains fine-scale spectral variability, and produces sharper narrow coastal precipitation bands and localized, terrain-sensitive extremes. Its broader-scale performance remains comparable to coarser-resolution configurations while preserving large-scale skill despite higher resolution. Dropsonde verification shows lower errors and improved categorical skill at the most extreme IVT threshold. Overall, West-WRF AI 2-km provides its greatest value for localized precipitation extremes and intense AR-related moisture transport.

physics.ao-ph↗
arXiv2026

Hydrological Constraints on Temperature Sensitivities of Precipitation Frequency and Intensity

To improve understanding of how precipitation frequency and intensity change with warming, we combine the coupled land--atmosphere water balance with a stochastic hydrological model that retains explicit dependence on precipitation frequency and event depth. We find a linear relationship between co-variations of rainfall frequency and intensity, where the slope is a hydrological sensitivity determined by the dryness and storage indices and the intercept is a forcing term associated with changes in moisture convergence, potential evapotranspiration, and effective water-storage capacity. While a given relative change in either rainfall frequency or intensity produces an identical relative change in the mean rate, rainfall intensity additionally influences the runoff coefficient by altering terrestrial water storage relative to rainfall depth. This means frequency and intensity have different impacts on the water cycle, allowing the frequency--intensity slope to depart from -1. Analysis of long-term MOPEX records shows that the forcing term is dominated by moisture convergence and that the frequency--intensity relation becomes steeper from wet to dry regimes, consistent with the slope predicted by the theory. The framework provides a physical interpretation of the observed negative covariation between precipitation frequency and intensity and clarifies the roles of hydrological partitioning and atmospheric moisture supply in their responses to warming.

physics.ao-ph↗