We calibrated impact functions for hazard and exposure resolutions of 1, 4 and 10 km as displayed in Fig. d–f with the methodology described above. Damage estimates for past events are displayed against the reported damage data, (Fig. a–c). Event location, duration and total economic damage were retrieved from the EM-DAT database. For the modelling we downloaded FIRMS data for the respective country and for the event duration as indicated in EM-DAT. Information on the locations affected by the fires is reported highly heterogeneously, but it is always available at least on an admin 1 level (i.e. state level in the US). Hence, to allow for consistency, damage estimates were accumulated to admin 1 level. After calibrating for the three resolutions, the RMSF cost function (Eq. ) was minimized best with 1 km resolution, equalling 20.8 (for Ihalf=295.0 K). At this high resolution, the impact function converges to a step function (Fig. a), which could be interpreted as all assets being destroyed wherever a fire is detected. We were concerned that the total exposure under higher-resolution footprints was not enough to recreate EM-DAT losses, resulting in the 100 % damage step function, but since damage estimates are not negatively biased we ruled this out. The model performed nearly equally well on a 4 km resolution, where a minimum RMSF of 22.6 was found (for Ihalf=409.4 K), which results in a smoother shape of the impact function. At 10 km resolution the model performed worse, with a minimum RMSF of 35.1 (for Ihalf=484.4 K). The obtained RMSF are well in line with impact function calibrations for other hazards, e.g. tropical cyclones where RMSF values in the range of 16.8–22.2 were found . Although uncertainties remain substantial (see also Sect. ), our approach performs well on the order of magnitude, as the two dotted lines in Fig. a–c indicate. This is true for 55 out of 86 events (64 %) at a 1 km resolution and 54 out of 86 events (63 %) at 4 km resolution (with 47/86 events (55 %) at 10 km). Most importantly, this ratio is even better for the most expensive events with reported damages of more than USD 1 billion, where 18 out of 21 (86 %) are estimated in the correct order of magnitude for 1 km resolution (76 % for 4 km and 62 % for 10 km). This is of great importance, as such events are of special interest to society and stakeholders. Looking at differences within 2 orders of magnitude, model estimates are correct for 90 % of the events on the 1 and 4 km scale (84 % for 10 km). We also conducted experiments on coarser resolutions (20 km, not shown); however, the results became inconclusive. We refrained from calibrating the model for resolutions below 1 km, as the LitPop approach is not suited for such assessments because detailed local features would gain relevance .

The model shows no systematic error for individual continents. However, given that most reported damage data stems from the USA and Australia, the calibration is likely biased towards these regions. The model did not produce an impact for one instance in Chile, where EM-DAT reports an event in the province of Coquimbo during February 2002 with an impact of USD 100 million, but FIRMS data show no relevant fire activity in that province during that time span.

While not all events were individually investigated, we found our underestimations of damages are often linked to damages to rural assets, such as national park infrastructure, or expensive agricultural assets, such as timber resources or vineyards. In these cases, the exposure at peril is underrepresented, as the night-time luminosity of such assets is low. As an example, the greatly underestimated Great Smokey Mountains wildfire (reported damage of USD 1.2 billion, estimated damage of USD 30 000) that occurred in Tennessee in 2016 caused massive damage to a national park for which the infrastructure is not well represented in our exposure layer.

On the one hand, overestimates can sometimes be linked to damages along the wildland–urban interface (WUI), where even at 1 km resolution sub-grid information is required to precisely represent this critical boundary. Generally, increasing the resolution of the exposure layer (while keeping the hazard resolution constant) yields better results for all hazard resolutions (see Fig.  in the Appendix). On the other hand, increasing the resolution of the hazard yields steeper impact functions, which are not dependent on the exposure layer. This finding is important for the model's capability to work with different sources of exposure data (see Fig.  in the Appendix).

In order to more closely assess model output on direct economic damages, we performed two case studies – one for the prominent 2019/2020 wildfire season in Australia and the other one for the January 2017 Chilean wildfires. While Chile is a comparably data-scarce country, the CLIMADA modelling approach requires no country-specific adjustment and thus facilitates studies in countries of the Global South. Both studies are conducted at a resolution of 4 km for hazard and exposure and with the impact function obtained from our calibration. We chose to use a resolution of 4 km as the calibration revealed that the model performs only slightly better on a resolution of 1 km. Hence, the higher resolution does not reliably provide additional value, while the potential errors in exposure disaggregation increase with higher resolution.

In this study we present and describe a newly developed and calibrated model to assess economic damages of wildfires globally but at a high resolution. This has been identified as a particularly under-researched field . The model builds on the CLIMADA modelling platform, which is a broadly used tool for natural hazard impact assessment. The model produces sound estimates of wildfire damages on scales of 1 and 4 km and reasonable estimates on a scale of 10 km. Its capabilities in estimating impacts are well in line with well-established global impact models for natural hazards such as tropical cyclones . The improvement in damage estimates going from 4 to 1 km is relatively minor. We therefore expect that information on local exposure characteristics and exposure-specific vulnerability curves is likely to be more important to model improvements than further increases in exposure or hazard resolution. However, we refrained from working with better resolved regional data because this would conflict with our globally consistent approach. While the model results are less precise on a 10 km scale, we still regard this as a useful setup for coupling with regional climate models that are approaching such resolutions . Furthermore, for many practical applications, such as financial risk disclosure, information on exposure is often available at a relatively coarse resolution (e.g. ZIP code level).

We deliberately refrained from producing traditional risk metrics such as exceedance frequency curves or time series analyses as we suspect that the analysis of past data would lead to an underestimation of current wildfire risk due to the strong inherent climate trend. However, as the FIRMS data are available in near real time, the model is well suited for rapid impact estimates, which are crucial for efficient disaster response and recovery (e.g. insurance payments or governmental response).

The CLIMADA platform provides interoperability with custom exposure data sets, given that they contain information on latitude, longitude, and exposed value. The calibrated vulnerability curves of this study might serve as a valid starting point for impact calculations with bespoke exposure data. However, especially at high resolution (<1 km), exposure-specific features gain importance, e.g. the distance between infrastructure and vegetation. Hence, a re-calibration might be required.

The model is open source and open access and can be applied to any location in the world, as it is designed to depend solely on freely available and easily accessible global data sets. Bespoke regional data might easily be included by users, given the open architecture of the approach.

As with any impact modelling, assessment of economic impacts due to wildfires is subject to major uncertainties. Here, we discuss uncertainties that are inherent in this model. Identifying and addressing these uncertainties might also guide future research questions. Following , we distinguish between the sources and the nature of the uncertainties. The nature of uncertainty can be parted into epistemic and stochastic (or aleatory) uncertainty. The epistemic uncertainty can be understood as the uncertainty due to imperfect knowledge and the stochastic uncertainty as the uncertainty due to inherent variability . The relevant sources of uncertainties are data and model uncertainty, and we will discuss their epistemic and stochastic uncertainty.

Looking at the epistemic data uncertainty, a major portion is due to the general lack of impact data. EM-DAT draws information from various different sources with no widely agreed-upon reporting standard . In addition, the blending of direct and indirect economic damages further enlarges the uncertainties. Thus, reported figures should not be considered hard data but rather rough estimates that come with uncertainties up to nearly an order of magnitude themselves . Furthermore, due to smaller reporting capabilities, uncertainties are likely bigger in poorer countries and thus within the most vulnerable communities. Finding reliable damage information is even harder when we look for data on a sub-national scale. The model has therefore been built to provide consistent impact estimates with similar precision to the source data and without systematic biases. Large uncertainties are also present within the exposure data. As LitPop hinges on night light luminosity and population density, agricultural assets can be substantially underestimated, as a vineyard is hardly differentiated from a fallow field. On the other hand, a motorway that is brightly illuminated during the whole night can lead to overestimations of exposure . The fire detection error of MODIS data is 1.2 % . Hence, in comparison to the other data sources, the hazard data come with little uncertainty: given the shape of the impact functions, small differences in fire intensity do not affect damage estimates very strongly. However, small forest clearings can register as false fire detections, while thick smoke might obscure large fires; therefore, fire extent data are also not perfect .

On the side of the epistemic model uncertainty, the uncertainties stem from the design of CLIMADA, its wildfire module, and the choice of its parameters. In this study, we estimate impacts solely based on the heat of a fire – this is a strong simplification, as it is known that fires attack infrastructure through other processes, such as ember attack . We also do not include major drivers of economic losses in our model, such as smoke, health costs, fire suppression costs, business interruption or loss of tourism . Given the source data uncertainty and our need for a globally consistent approach, we decided that a simpler model with fewer tunable parameters is more transparent, and just as able to reproduce the reported data, given the other epistemic uncertainties.

Finally, fire risk modelling is subject to major stochastic uncertainty. Although influenced by many factors, the spread of wildfires is chaotic. Whether or not a building catches fire or whether a fire is detected sufficiently early remain subject to (bad) luck. The co-location of fire and exposure in a model grid cell could lead to 0 % or 100 % damage. Thus, as is common in natural hazard impact modelling, uncertainties will always remain a major component of any results. Future work will be able to quantify this uncertainty and provide confidence intervals for losses.