Weather Data on Strava and Running Apps: Complete Guide to Performance Analysis

Every run you record includes more data than you probably realize. Beyond pace, distance, and heart rate, most running apps capture weather conditions at the time of your run—temperature, humidity, wind, and more. This weather data sits in your training history, often unexamined, holding insights that could transform how you understand your running. Why were those miles last August so much harder than the same route in October? Why did your tempo run fall apart when conditions looked fine? The answers are buried in the weather data attached to every run, waiting to reveal patterns about how conditions affect your performance and what your optimal running environment actually looks like.

This guide covers everything about weather data in running apps: what gets logged and where to find it, how to analyze historical weather patterns, using this data to understand your performance, and bridging the gap between retrospective data and predictive planning.

What Weather Data Gets Logged

Temperature and Feels-Like Temperature

The baseline weather metrics:

Actual temperature:

The measured air temperature at your run time
Usually pulled from nearby weather stations
Recorded at run start (some apps update throughout)
The objective number, not necessarily how it felt
Useful for comparing across runs

Feels-like temperature:

Accounts for wind chill in cold weather
Accounts for heat index in warm weather
More relevant to your actual experience
Better for understanding performance impact
What your body actually deals with

Temperature variations during runs:

Temperature can change significantly during longer runs
Morning runs often start cool and warm up
Evening runs may cool as sun sets
Some apps log start temperature only
Others capture average or multiple readings

What temperature data reveals:

Your performance curve across temperatures
Optimal temperature range for your running
How much heat or cold affects you personally
Seasonal patterns in your training
Comparison points for race conditions

Humidity and Dew Point

The moisture factor often overlooked:

Humidity recording:

Relative humidity at time of run
Percentage of moisture in air
Varies significantly by time of day
Crucial for understanding effort in summer
Often not examined by runners

Dew point (when available):

Some apps record dew point
More meaningful than relative humidity
Absolute measure of moisture content
Above 60°F starts affecting performance
Above 65°F significantly challenging

Why humidity data matters:

Explains hard runs on "moderate" temperature days
Reveals why some mornings feel worse than afternoon
Shows your humidity sensitivity
Helps set expectations for conditions
Explains heart rate drift patterns

The humidity-performance connection:

High humidity prevents effective sweating
Same temperature feels very different at different humidity
Your pace naturally slows in humid conditions
Understanding this prevents discouragement
Data validates the experience

Wind Conditions

The invisible performance factor:

Wind speed recording:

Miles per hour or kilometers per hour
Usually from nearby weather stations
May not reflect your actual route conditions
Urban vs. open areas differ significantly
Directional data less commonly logged

What wind data shows:

Runs that felt harder may show high wind
Slow splits into headwind explained
Fast splits with tailwind contextualized
Overall effort adjustment needed
Route selection implications

Wind's unique challenges:

Unlike temperature, hard to remember accurately
Runners often don't notice moderate winds
But performance data shows the impact
Historical wind data provides objective record
Explains outlier runs

Limitations of logged wind data:

Station readings may not match route conditions
Buildings block wind in urban areas
Trails may be sheltered differently
Direction relative to route matters
Take wind data as approximate

Weather Conditions

The qualitative picture:

Common condition categories:

Clear/Sunny
Partly cloudy
Cloudy/Overcast
Light rain/Drizzle
Rain
Snow
Fog

What conditions reveal:

How often you run in various conditions
Whether rain affects your performance
Cloud cover preferences
Your all-weather running patterns
Seasonal condition trends

Condition vs. performance:

Some runners perform better in overcast
Clear, sunny isn't always optimal
Rain can be faster (if cool)
Conditions affect psychology too
Personal patterns emerge over time

Where to Find Weather Data

Strava

Accessing weather on the platform:

On individual activities:

Weather widget on activity page
Shows temperature and conditions
Available on web and app
Check historic activities for data
May vary by location accuracy

Strava's data sources:

Pulls from weather APIs
Uses activity location
Recorded at time of activity
Generally reliable for major areas
May have gaps in remote locations

Using Strava's weather effectively:

Review hard run days to see conditions
Compare similar routes across conditions
Note patterns in your performance
Build mental database of preferences
Use for goal-setting context

Garmin Connect

Weather integration in Garmin's ecosystem:

What Garmin logs:

Temperature (often very accurate)
Weather conditions
Some devices have sensors for temperature
Data synced to Garmin Connect
Visible on activity details

Finding the data:

Activity details page
Weather section in activity
Historical activities retain weather
Exportable with activity data
Third-party analysis possible

Garmin's accuracy:

On-device temperature sensors on some watches
Can be more accurate than station data
But body heat affects wrist sensors
External temperature often more reliable
Cross-reference when possible

Apple Fitness and Health

Apple's approach to weather:

Weather data in workouts:

Temperature logged with outdoor workouts
Humidity sometimes included
Weather conditions captured
Location-based data
Viewable in Fitness app

Integration with other apps:

Data available to health-connected apps
Can export for analysis
Part of overall workout record
Works with Apple Watch runs
iPhone-only runs may have less data

Other Running Apps

Weather across the ecosystem:

Nike Run Club:

Basic weather data
Temperature primarily
Conditions occasionally
Visible on run details
Less emphasis on weather

COROS, Suunto, Polar:

Various levels of weather integration
Check your specific platform
Export options vary
Third-party analysis possible
Weather increasingly standard

Running app weather features:

Trending toward more weather data
Recognition of weather's importance
Better integration over time
Historical data increasingly available
Competition driving improvements

Analyzing Your Weather Data

Finding Patterns in Performance

Using data to understand yourself:

The temperature analysis:

Review runs across temperature range
Note pace at various temperatures
Control for other variables (distance, terrain)
Identify optimal temperature zone
Recognize performance drop-off points

How to do temperature analysis:

Export or manually compile data
Group runs by temperature bands (50-55, 55-60, etc.)
Compare pace for similar effort runs
Look for consistent patterns
Account for other factors (training phase, freshness)

Humidity pattern recognition:

Same process for humidity or dew point
May require manual data extraction
Compare runs with similar temperature but different humidity
Note how humidity affects you
Some runners are more affected than others

Seasonal analysis:

Compare same route across seasons
Control for fitness changes
See how conditions change performance
Identify your best running season
Plan training around these insights

Heart Rate and Weather

The cardiovascular weather connection:

What to look for:

Same pace, different heart rate across conditions
Heart rate drift patterns in heat
Easy days that weren't easy (by heart rate)
Correlation between conditions and cardiac load
Personal threshold identification

Heat and heart rate:

Heart works harder in heat
Same pace requires higher HR
This is normal physiology
Data quantifies the effect for you personally
Helps set appropriate expectations

Cold and heart rate:

Generally lower heart rate in cold
But extreme cold has its own stress
Optimal range for efficient running
Personal patterns vary
Worth understanding your response

Using HR-weather connection:

Adjust pace expectations for conditions
Set heart rate zones for current weather
Validate perceived effort
Plan workouts based on conditions
Avoid overtraining in challenging weather

Race Performance Analysis

Weather context for race results:

Post-race weather review:

Check conditions during your race
Temperature, humidity, wind
Compare to your training conditions
Understand performance in context
Set appropriate future expectations

PR conditions:

Note weather when you set PRs
These are your proven optimal conditions
Target similar conditions for goals
Don't expect PRs in worse conditions
Plan race selection accordingly

Disappointing race analysis:

Review conditions for races that didn't go well
Weather may explain performance
Identify if conditions were worse than expected
Use for future race planning
Don't blame yourself for weather

Building race condition preferences:

Compile data across races
Know your optimal race weather
Select races with favorable weather probability
Set condition-adjusted goals
Use weather as race selection criterion

Practical Applications

Training Decisions Based on Weather Data

Using insights to train smarter:

Workout scheduling:

Schedule quality workouts for favorable conditions
If you know heat hurts your tempo, avoid it
Use weather-friendly days for speed work
Easier runs can absorb weather challenges
Flexibility based on personal data

Pace adjustment confidence:

Historical data supports pace changes
If you consistently run 20 sec/mile slower above 70°F, plan for it
Objective basis for adjustment
Not making excuses—making smart decisions
Data-driven pacing

Training load management:

Challenging weather is training load
Factor into recovery decisions
Hard workout in heat = harder than same in cool
Use weather data to assess true load
Prevent overtraining from weather ignorance

Long run planning:

Long runs are most affected by heat
Know your long run temperature ceiling
Plan routes, times, and conditions
Historical data guides decisions
Consistency over suffering

Goal Setting and Race Selection

Weather-informed goals:

Realistic goal setting:

Base time goals on achievable conditions
Know your weather performance curve
Don't set a goal based on cool-weather runs for a hot race
Condition-adjusted goals
A/B/C goals for weather scenarios

Race selection:

Choose races with favorable weather probability
Know what conditions you need for goals
Historical race weather helps selection
Late fall/early spring often best
Avoid summer goal races unless you're acclimated

The PR weather profile:

Compile conditions of your best runs
Ideal temperature range
Acceptable humidity level
Wind tolerance
Conditions to seek for goals

Weather as a training variable:

Some training in challenging conditions is valuable
But quality workouts need quality conditions
Use weather data to balance
Planned adversity vs. random suffering
Smart training uses weather strategically

Tracking Improvement Over Time

Longitudinal weather analysis:

Same-conditions comparison:

Compare runs at similar conditions over time
Control for weather to see fitness change
More accurate than raw pace comparison
Validates training improvement
Encourages during tough weather periods

Seasonal improvement:

Compare this summer to last summer
Adjust for conditions
See fitness gains independent of weather
Motivating perspective
Shows training is working

Condition tolerance improvement:

Are you handling heat better?
Has cold performance improved?
Adaptation is trainable
Data shows acclimatization
Validates deliberate condition training

The Difference Between Reactive and Predictive

Strava's Retrospective Model

What recorded weather provides:

The value of historical data:

Explains what happened
Builds your personal weather database
Informs future decisions
Teaches you about yourself
Essential for pattern recognition

The limitation:

Only know conditions after the run
Can't change past decisions
Reactive, not proactive
Learning curve takes time
Doesn't optimize current decisions

Predictive Weather Planning

What forward-looking weather offers:

The predictive advantage:

Know conditions before you run
Choose optimal time windows
Schedule workouts for good conditions
Avoid running in dangerous weather
Proactive optimization

How prediction changes behavior:

Check forecast, then decide when to run
Move workouts to better days
Avoid conditions you know affect you
Plan the week around weather
Training decisions based on upcoming conditions

Run Window's approach:

Combines prediction with personalization
Shows optimal running windows
Looks forward, not back
Uses weather forecast for decision support
Real-time guidance

Using Both Together

The comprehensive approach:

Historical data builds knowledge:

Understand your personal weather patterns
Know what conditions work for you
Recognize your temperature/humidity curve
Identify optimal and dangerous zones
Foundation for future decisions

Predictive planning applies knowledge:

Apply your learned patterns to forecasts
Make proactive decisions
Optimize running schedule
Avoid conditions you know are problems
Use knowledge in real-time

The complete runner:

Reviews historical weather performance
Learns personal patterns
Uses forecasts to optimize future runs
Adjusts expectations for conditions
Weather-aware at every level

Key Takeaways

Your running apps log weather data. Check your activities for temperature, humidity, and conditions.
Historical weather data reveals patterns. Analyze how conditions affect your performance to understand yourself.
Temperature affects pace consistently. Your data likely shows a performance curve across temperatures.
Humidity matters more than you might think. Review tough runs—high humidity often explains the struggle.
Heart rate and weather are connected. Same pace at higher heart rate in heat is normal and documented in your data.
Race performance requires weather context. Understand conditions when analyzing results.
Historical data is reactive. It teaches but doesn't predict—use it to build knowledge for future application.
Combine retrospective and predictive approaches. Learn from past data, apply to future decisions with forecasts.

Understanding how weather affects your running starts with the data you already have. Run Window adds predictive power—showing when to run before you head out the door.