Choosing the right climate risk platform is a critical decision for organisations seeking to understand, manage, and act on physical climate risks. Whether you're evaluating asset-level exposure, planning infrastructure resilience, or integrating climate intelligence into strategic workflows, the tools you use must be scientifically robust, scalable, and decision-useful.
Two leading platforms in this space are EarthScan, by Mitiga Solutions, and XDI. Both offer global coverage, hazard modelling, and scenario analysis, but they differ in their scientific foundations, modelling approaches, usability, and flexibility.
This article provides a clear, side-by-side comparison of EarthScan and XDI across key features that matter most to decision-makers: hazard coverage, resolution, uncertainty quantification, return periods, platform usability, and more.
Whether you're in finance, energy, infrastructure, or government, this guide will help you assess which solution best fits your climate risk needs.
Quick summary table: EarthScan vs XDI
Key features comparison
Each feature below is explained in terms of why it matters, how EarthScan approaches it, and how XDI compares.
1. Hazard types and scenario coverage
Understanding which hazards are covered, and under what climate scenarios, is critical because it defines the scope and real-world relevance of your climate risk insights.
Different hazards affect different regions and asset types, and their severity changes depending on future emissions pathways. Without full hazard and scenario coverage, you risk missing key threats or underestimating long-term exposure.
Robust scenario coverage ensures you can assess both acute hazards (like floods or wildfires) and chronic trends (like heat stress or drought) across multiple timeframes and futures, enabling smarter planning, investment, and compliance.
EarthScan approach
EarthScan covers 11 hazards acrosss 6 categories:
- Extreme temperature
- heatwave
- heat stress
- changing temperature
- Drought
- consecutive dry days
- Extreme wind
- Storm (blizzards)
- Storm (blizzards)
- Precipitation risk
- Flooding
- coastal flooding
- sea level rise
- fluvial flooding
- Wildfire
- wildfire
The platform uses CMIP6 global climate models, CORDEX regional downscaling, and proprietary hazard simulation engines. Supported scenarios include are based on the IPCC’s Shared Socioeconomic Pathways (SSPs), including:
- Business-as-usual (SSP5-8.5)
- Paris-aligned (SSP1-2.6)
- Emissions peak (SSP2-4.5)
These pathways allow users to explore a wide range of plausible futures, from high-emissions trajectories to low-carbon transitions.
XDI comparison
XDI covers 11 hazards, including freeze-thaw cycles, landslides, and tropical cyclone surge. It uses CMIP5 and CMIP6 climate models, and its Climate Risk Engines apply engineering-based methods combined with bespoke probabilistic algorithms to assess asset vulnerability and hazard impacts.
Scenarios are mapped from RCPs to NGFS pathways (e.g. RCP8.5 to Current Policies), but public documentation does not confirm the use of IPCC’s SSPs or whether users can toggle between scenarios.
Outputs are expressed in financial and engineering metrics such as Failure Probability and Maximum Value at Risk. While probabilistic methods are used internally, the platform does not specify whether final outputs include quantified uncertainty ranges such as confidence intervals.
2. Scientific rigour and modelling approach
Credibility is the cornerstone of any climate risk assessment. For results to be trusted and actionable, they must be grounded in robust science, transparently modelled, and clearly interpretable by decision-makers across sectors.
A credible platform is built on four key pillars:
- Scientific rigour: Incorporating the latest climate science, including global and regional models such as CMIP6 and CORDEX, ensures that risk assessments reflect the most up-to-date understanding of climate dynamics.
- Transparency: Clear documentation of modelling assumptions and data sources allows users to understand how results are derived and how they relate to real-world conditions.
- Interpretability: Outputs must be structured in a way that supports decision-making, from investment and insurance pricing to regulatory reporting without requiring specialist knowledge to decode.
- Trust: When results are defensible under scrutiny from regulators, auditors, and internal stakeholders, they become a reliable foundation for strategic action.
Without these elements, climate risk assessments risk being incomplete, misleading, or non-compliant, especially in high-stakes contexts where accuracy and accountability are paramount.
One of the most important distinctions in climate risk modelling is the difference between probabilistic and deterministic approaches.
A probabilistic model, like EarthScan’s Multiple Futures Model (MFM), doesn’t just provide a single outcome, it generates a range of possible futures, each with an associated likelihood. This allows users to see not only what might happen, but how likely each outcome is.
This is especially important for fat-tailed risks, such as extreme weather events, where the probability of very severe outcomes is higher than in a normal distribution. A probabilistic approach enables more accurate modelling of these rare but high-impact events, which are critical for infrastructure planning, insurance pricing, and regulatory stress testing.
It also allows EarthScan to provide quantified uncertainty for every output, including confidence intervals and return periods from 2 to 1,000 years, giving users a clearer picture of risk and the ability to interrogate the robustness of results.
EarthScan approach
EarthScan combines climate science with statistical science via its MFM, a Bayesian framework that quantifies uncertainty and improves predictive skill.
It integrates CMIP6 and CORDEX data with 40+ years of observational records from over 100,000 stations in 180 countries. Statistical techniques like Gaussian Process Regression are used to fill data gaps and enhance accuracy.
XDI comparison
XDI’s Climate Risk Engines use engineering-based methods to assess asset vulnerability and simulate hazard impacts. According to XDI’s public documentation, these models combine dynamically downscaled climate data with global and local datasets, applying bespoke probabilistic algorithms to produce financial and risk metrics.
Outputs are expressed in engineering and financial terms, such as Failure Probability (FP%) and Maximum Value at Risk (MVAR%). While these metrics reflect hazard severity and asset vulnerability, XDI does not publicly describe the use of probabilistic frameworks like Bayesian inference, nor does it provide confidence intervals or uncertainty ranges in its outputs.
As of the latest available information, quantified uncertainty is not a core feature of XDI’s user-facing results, and scenario toggling or SSP alignment is not explicitly documented.
3. Resolution and geographic coverage
Resolution and geographic coverage are foundational to the credibility and usefulness of any climate risk assessment. High-resolution data allows organisations to evaluate risk at the level of individual assets, not just cities or regions, which is essential for making informed decisions about infrastructure investment, adaptation planning, and regulatory compliance.
Without sufficient granularity, risk signals can be diluted or missed entirely, especially in areas with complex terrain or microclimates.
Equally important is the platform’s geographic coverage. A global portfolio demands consistent methodologies across jurisdictions, while local projects require detailed, location-specific insights. If a platform lacks coverage in key regions or applies inconsistent modelling approaches, it undermines the comparability and reliability of the results.
In short, resolution determines how precisely risk can be measured, and coverage determines where it can be measured. Together, they define the platform’s ability to support asset-level decision-making, meet disclosure requirements, and enable robust financial modelling.
EarthScan approach
EarthScan downscales global models to resolutions as fine as 90 metres, using a combination of dynamical and statistical techniques. It applies bias correction and Gaussian Process Regression to fill data gaps and improve accuracy.
Coverage is global, with consistent methodologies across jurisdictions.
XDI comparison
XDI provides hazard data at resolutions ranging from 5 to 50 metres, depending on the hazard and region. This granularity supports detailed analysis, though resolution varies by hazard type and geographic availability.
4. Risk quantification and tail-event planning
Return periods help organisations plan for rare but high-impact events, essential for resilience planning, insurance underwriting, stress testing, and infrastructure design.
Without them, risk assessments may underestimate exposure to extreme events.
EarthScan approach
EarthScan models return periods of 2, 5, 10, 20, 50, 100, 200, 500, and 1,000 years for key hazards with extended timeframes for perils like wind and wildfire.
This enables robust tail-event planning and supports insurance pricing and regulatory stress testing.
XDI comparison
XDI’s public documentation does not specify consistent return period modelling across all hazards.
The platform focuses on hazard exposure and asset vulnerability, with outputs expressed in engineering and financial metrics such as Failure Probability (FP%) and Maximum Value at Risk (MVAR%).
While return periods are used in some hazard models (e.g. surface water flooding), they are not presented uniformly across all outputs.
5. Climate data outputs and decision usefulness
Climate risk data is only valuable if it can be interpreted and acted upon. Decision-makers need outputs that are not just technically accurate, but also intuitive, scalable, and aligned with their operational and regulatory needs.
This includes clear risk ratings, financial metrics, and quantified uncertainty. All delivered in formats that integrate seamlessly into existing workflows.
EarthScan approach
EarthScan is designed to deliver decision-ready climate intelligence. Its outputs are fully probabilistic, location-specific, and require no asset-specific inputs, making it plug-and-play, scalable, and highly actionable.
Key outputs include:
- Risk ratings (A–F): A standardised scale that summarises exposure to climate hazards, useful for portfolio screening, prioritisation, and disclosure.
- Climate Value at Risk (CvAR): Estimates the percentage of direct damage an asset might suffer under specific hazard intensities, supporting financial planning and insurance modelling. This feature is currently available for wind and flooding hazards.
- Confidence intervals: Bayesian-derived uncertainty bounds that quantify the range of possible outcomes, enabling stress testing and scenario planning.
- Return periods (2–1,000 years): Available for all hazards, with extended ranges for flooding and wind, allowing users to assess both frequent and extreme events.
- Statistical signals: Trend detection tools that highlight changes in hazard intensity or frequency over time.
- Downloadable reports and API access: Outputs can be accessed via a user-friendly platform or integrated directly into internal systems through a scalable API (3.5M+ calls/month).
EarthScan’s probabilistic approach is particularly valuable for modelling fat-tailed climate risks, events that are rare but disproportionately severe, such as extreme floods or heatwaves. These events occur more frequently than traditional models assume.
By capturing the full distribution of possible outcomes, EarthScan enables users to assess both the likelihood and severity of extreme events, supporting more resilient planning and risk pricing.
XDI comparison
XDI offers risk scores and hazard metrics such as Failure Probability (FP%) and Maximum Value at Risk (MVAR%), which are traceable to specific asset components. Reports are packaged by sector and use case, and are suitable for internal briefings and external reporting.
While XDI applies probabilistic algorithms internally, its outputs are presented as point estimates rather than full probability distributions. The platform does not provide confidence intervals or uncertainty ranges, which limits users’ ability to assess the variability or robustness of results across different climate futures.
6. Reporting and disclosure readiness
Climate risk disclosures are becoming mandatory in many jurisdictions, particularly under regulations like CSRD. Frameworks such as TCFD and standards like IFRS S2 (from the ISSB) are shaping how organisations report physical climate risks, but adoption and enforcement vary by region.
Platforms must support automated, jurisdiction-specific reporting to ensure compliance and reduce manual effort.
EarthScan approach
Disclose is Mitiga Solutions’ dedicated tool for regulatory reporting. It supports alignment with over 30 climate-related disclosure requirements, including the CSRD (via ESRS E1), IFRS S2, TCFD, and NGFS guidance.
Users can upload asset lists and receive disclosure-ready reports in minutes.
XDI comparison
XDI supports climate risk disclosure through standardised reports aligned with TCFD, IFRS S2 (via ISSB), EU Taxonomy, and SEC requirements. These reports are suitable for both internal briefings and external reporting, and XDI has supported regulatory stress testing across multiple regions including Australasia, Europe, the UK, and North America.
While XDI does not offer a dedicated reporting tool, its standardised reports are designed to meet global disclosure expectations and have been used in regulatory contexts.
7. Commercial model and pricing flexibility
Flexible pricing and accessibility are key for scaling climate risk analysis across teams and portfolios. Rigid contracts can limit adoption and delay implementation.
EarthScan approach
EarthScan offers:
- No minimum contracts
- Volume-based discounts
- Affordable API access
- Unlimited users
This makes it accessible for organisations of all sizes.
XDI comparison
Pricing is not publicly disclosed. Access is typically via enterprise contracts and reseller networks. The platform supports bespoke solutions through its reseller network, but pricing flexibility is not detailed.
8. Platform speed and usability
Speed and ease-of-use affect adoption, integration, and operational efficiency. A platform that’s slow or complex can hinder decision-making and reduce ROI.
EarthScan approach
EarthScan is a fully functional SaaS platform with:
- CSV upload
- API integration
- Instant ratings and reports
- Scalable to 3.5M+ API calls/month
Its intuitive interface supports fast onboarding and seamless integration.
XDI comparison
XDI offers a secure platform with fast screening and portfolio analysis. Users can access data via the Climate Risk Hub, API, or off-the-shelf reports. While the platform supports tailored solutions, features such as CSV upload and automated reporting are not highlighted in public documentation.
Tip: What to ask when comparing platforms?
- Ask about uncertainty quantification: Many platforms offer hazard exposure, but few quantify uncertainty. EarthScan’s Bayesian modelling gives you confidence intervals and probabilistic outputs, essential for stress testing, scenario planning, and understanding the full range of potential outcomes.
- Check regulatory coverage: If you’re reporting under ESRS E1 CSRD or IFRS S2, make sure your platform supports those standards. Disclose covers 30+ jurisdictions.
- Look for return periods: Tail-event planning requires more than averages. EarthScan offers up to 1,000-year return periods for all hazards.
EarthScan vs XDI: Which one to choose?
If your organisation needs:
- Scientifically rigorous, probabilistic climate risk analysis
- Full regulatory alignment across CSRD, TCFD, IFRS S2
- High-resolution, asset-level insights across global portfolios
- Scalable, user-friendly tools with flexible pricing
- Decision-ready outputs designed for planning, investment, and disclosure
Then EarthScan, by Mitiga Solutions, offers a comprehensive solution built for future-proof climate intelligence.
XDI provides robust hazard modelling and has supported regulatory stress testing. Its engineering-based approach delivers detailed asset-level insights. For organisations prioritising probabilistic modelling, quantified uncertainty, and broad regulatory coverage, EarthScan may offer a more complete solution.
Final words
Choosing the right climate risk platform is a strategic decision that affects compliance, investment, and resilience planning. EarthScan combines scientific rigour, intuitive usability, and full regulatory alignment to deliver actionable climate intelligence.
Whether you're screening assets, preparing disclosures, or planning for long-term resilience, EarthScan delivers the clarity, confidence, and coverage your organisation needs. Ready to see EarthScan in action? Book your demo today.
