# Climate adaptation in the southwest US: The SWPar4.5 parameter set for stochastic weather generators

**Authors:** Andrew T. Fullhart, Shang Gao, Wenting Wang, Emile Elias, Gerardo Armendariz, David C. Goodrich

PMC · DOI: 10.1038/s41597-025-06102-5 · 2025-11-19

## TL;DR

The paper introduces a new climate parameter set for generating detailed local weather data in the southwestern US.

## Contribution

The SWPar4.5 set enables high-resolution point-scale climate modeling using fused gridded data under RCP4.5.

## Key findings

- SWPar4.5 generates historical and future point-scale climate data at ~800 m resolution.
- Local precipitation intensity increases are observed, affecting erosion and runoff.
- The parameter set fuses climate projections to capture sub-daily precipitation patterns.

## Abstract

Climate assessment in the southwestern US is complicated by extreme spatial gradients and short, intense rainfall events. In this region, gridded climate data, including commonly applied ~4 km daily datasets, often obscure spatial gradients and short-term precipitation dynamics. Contrasting, point-scale data (as measured by ground instruments like rain gauges) better reflects important precipitation factors and is preferred input for certain site-specific environmental models, including models classified by their domain as point-, plot-, field-, and hillslope-scale models. Facilitating targeted climate assessment, Southwest Parameter Set 4.5 (SWPar4.5) enables a framework for creating probable historical and future point-scale climate time series at ~800 m resolution. SWPar4.5 provides monthly climate benchmarks to parameterize a stochastic weather generator estimated using a data fusion of two existing gridded climate projections with differing spatiotemporal resolutions based on the middle ground representative concentration pathway scenario (RCP4.5). Resulting daily time series contain basic weather variables and include sub-daily precipitation patterns. Increases in precipitation intensity at local scale were found, with implications for soil erosion, runoff, and other environmental indicators.

## Full-text entities

- **Diseases:** GCM (MESH:D001037)
- **Chemicals:** water (MESH:D014867), CCSM4 (-)
- **Cell lines:** SWPar4.5 — Homo sapiens (Human), Adult acute megakaryoblastic leukemia, Cancer cell line (CVCL_2187), CCSM4 — Mus musculus (Mouse), Mouse melanoma, Cancer cell line (CVCL_B0CG)

## Figures

8 figures with captions in the complete paper: https://tomesphere.com/paper/PMC12630623/full.md

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Source: https://tomesphere.com/paper/PMC12630623