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climate-trends

Compute climate trends and ETCCDI extreme indices with Mann-Kendall and Sen's slope.

33|6|Updated Mar 17, 2026
Search Tags:#mann-kendall#climate trends#extreme indices#sen's slope#etccdi#xclim#ipcc figures
One-click install
npx skills add https://github.com/xjtulyc/awesome-rosetta-skills --skill climate-trends
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: climate-trends
Source: https://github.com/xjtulyc/awesome-rosetta-skills/tree/main/skills/04-earth-science/climate-trends
Command: npx skills add https://github.com/xjtulyc/awesome-rosetta-skills --skill climate-trends

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires xclim, scipy, matplotlib, numpy, xarray, pandas, cartopy.

What problem does it solve?

Climate trend analysis and attribution-style reporting require statistically defensible calculations of monotonic trends and changes in extreme precipitation and temperature indices across decades.

Core Features & Use Cases

  • Trend detection: Runs Mann-Kendall trend testing and estimates trend magnitude with Sen’s slope to support significance and direction in time-series climate studies.
  • Extreme indices (ETCCDI): Computes ETCCDI-style metrics such as RX1day, R10mm, TX90p, and other percentile/extreme frequency indicators using xclim-compatible workflows.
  • IPCC-style outputs: Produces research-ready visualizations and warming-stripe/IPCC AR6-inspired figures suitable for reports and publications, including “observed vs. model-projection” comparisons.

Quick Start

Use the climate-trends Skill to analyze a daily temperature and precipitation dataset and generate trend results plus ETCCDI extreme indices and IPCC-style figures.

Frequently Asked Questions about climate-trends

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I compute climate trends using Mann-Kendall and Sen's slope?▼

Compute climate trends by applying the Mann-Kendall trend test to detect significance and Sen's slope to estimate the magnitude of monotonic changes in temperature and precipitation time-series.

Can I calculate ETCCDI extreme climate indices with xclim?▼

Yes, you can calculate ETCCDI extreme climate indices using xclim-compatible workflows. This computes metrics like RX1day, R10mm, and TX90p for extreme precipitation and temperature frequency analysis.

What's the best way to generate IPCC-style figures for climate data?▼

Generate IPCC-style figures by processing daily climate datasets with xarray and matplotlib. This produces publication-ready visualizations including warming stripes and observed vs. model-projection comparisons.

Does this climate trend analysis work with ERA5 and CMIP6 datasets?▼

Yes, climate trend analysis works with both observed station/ERA5 and CMIP6 model daily datasets. It requires xarray and xclim-compatible unit-aware inputs to process the data.

When do I need unit-aware inputs for climate extreme indices?▼

Unit-aware inputs are needed when computing climate extreme indices to ensure accurate statistical outputs. Using xarray and xclim-compatible daily datasets prevents unit mismatch errors during trend and extreme event calculations.

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