Wildfire precursors show complementary predictability in different timescales

Domain: earth_science
Published: 2023-10-26
Editorial outcome: major_revision

Abstract
In most of the world, conditions conducive to wildfires are becoming more prevalent. Net carbon emissions from wildfires contribute to a positive climate feedback that needs to be monitored, quantified, and predicted. Here we use a causal inference approach to evaluate the influence of top-down weather and bottom-up fuel precursors on wildfires. The top-down dominance on wildfires is more widespread than bottom-up dominance, accounting for 73.3% and 26.7% of regions, respectively. The top-down precursors dominate in the tropical rainforests, mid-latitudes, and eastern Siberian boreal forests. The bottom-up precursors dominate in North American and European boreal forests, and African and Australian savannahs. Our study identifies areas where wildfires are governed by fuel conditions and hence where fuel management practices may be more effective. Moreover, our study also highlights that top-down and bottom-up precursors show complementary wildfire predictability across timescales. Seasonal or interannual predictions are feasible in regions where bottom-up precursors dominate.

Introduction
Fire weather is becoming more severe because of climate change1–3 and is expected to increase the number, extent, and severity of wildfires in many regions on Earth4,5. For example, the 2019/20 Black Summer wildfires in Australia were initiated and intensified by consecutive unprecedented dry years6, the unprecedented 2021 Siberia wildfires followed record-breaking heatwave and drought7, and the 2021 British Columbia wildfires followed widespread Northwest Pacific and Canada heatwave8. Wildfires play an important role in ecosystem dynamics and species preservation9,10. They can be beneficial to some ecosystems since smaller wildfires can prevent catastrophic wildfires by consuming fuels, or even promote species replacement and enhance their adaptation to climate change11,12. However, despite these positive effects, wildfires often have a negative impact on the environment and society, for example, through increased forest loss, carbon emissions, and smoke exposure13,14. Moreover, they contribute to climate warming by reinforcing a positive feedback loop15,16. In boreal ecosystems, this feedback loop may shift the carbon balance from sink to source17. Better understanding and management of wildfires, therefore, are necessary to mitigate their negative impacts on human livelihood and the Earth’s natural system. The availability of fuel is highly influenced by previous and current vegetation status and is a precondition for wildfires, yet extensive burning only occurs when fuels are dry and weather conditions are conducive to wildfire spread18. The Canadian Forest Wildfire Weather Index (FWI) system19 is widely used in wildfire studies to indicate fuel moisture, wildfire spread, fuel availability, and wildfire intensity by integrating temperature, relative humidity, wind speed, and precipitation over different time lags. The FWI system was originally developed for boreal ecosystems, although it has been used more widely3. However, the wildfire drivers vary at a global scale, and this variability is not fully understood because of the lack of mechanistic knowledge of ecosystem-specific wildfire precursors (or predictors).

Here, we apply a causal discovery algorithm considering lagged influences to detect the relationships between top-down weather and bottom-up fuel precursors and wildfires (in terms of burned area per climate and land cover class). This causal discovery algorithm, the Peter & Clark Momentary Conditional Independence (PCMCI)20,21, uses delayed time series to automatically and conditionally select relevant variables from high dimensional data sets and enable a meaningful causal interpretation21. We focus on investigating relationships between global wildfire occurrence, more specifically quantified in terms of burned area (BA), and wildfire precursors, for the period 2003–2020. The wildfire precursors were separated into two groups: (i) top-down group, including maximum air temperature (Tmax), vapor pressure deficit (VPD), potential evaporation (ET0), wind speed (Wind), and aridity anomaly index (AAI); (ii) bottom-up group, including the fraction of photosynthetically active radiation (FPAR), gross primary production (GPP), normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and soil water deficit index (SWDI). The details and selection of these precursors can be found in the Methods and Supplementary Methods.

Results
Spatial pattern of dominant precursors for wildfires
To investigate the dominant wildfire precursors globally, we applied PCMCI to build causal networks for each of the 232 ecoregions (29 climate zones multiplied by 8 vegetation types), using the top-down weather and bottom-up fuel grouping system. Then, in each ecoregion, we may detect a dominant precursor belonging to either the top-down or bottom-up group according to whether there is a causal relationship and the strength of the causal relationship (in this case partial correlation). High wildfire predictability is found in southeast North America, the tropical rainforests, the Sahel of Africa, the Congo Basin, the East European Plain and West Siberian Plain, the eastern Central Siberian Plateau, southeast China, and central and western Australia (Fig. 1a). Globally, the top-down group shows more dominance than the bottom-up group, accounting for 73.3% and 26.7% of the globe where causal relationships are detected (Fig. 1c). The bottom-up dominance is found mainly in the North American and European boreal forests, western North America, the African and Australian savannahs, and eastern South America (Fig. 1b). The other regions are dominated by the top-down group.

We ranked the importance (according to the dominant area fraction/pixel number) of the precursors in each group (Supplementary Fig. 1). In the top-down group, ET0 is the most important precursor followed by VPD, AAI, Tmax, and Wind, accounting for 41.1%, 32.0%, 16.6%, 10.3%, and 0% of top-down dominant regions, respectively. In the bottom-up group, SWDI is the most important precursor followed by GPP, EVI, FPAR, and NDVI, accounting for 56.4%, 21.4%, 11.1%, 8.6%, and 2.5% of bottom-up dominant regions, respectively. Wind speed is usually believed to play an important role in wildfire spread, while we detected almost no dominance globally. There could be two reasons: (1) wind speed gains dominance only when wildfires are ignited, while our study involved both fire season and non-fire season. A similar example is wildfires in the western United States are decoupled from lighting activities unless weather and vegetation conditions are considered22; (2) wind can both promote and suppress wildfire23, and this opposite effect diminishes its dominance.

The distribution of partial correlations for the two groups is shown in Fig. 1d. Wildfires are positively related to top-down precursors globally (91.6% positive). This positive relationship indicates that in flammability-limited regions, BA will increase when more energy is supplied (higher Tmax and ET0) or the environment is drier (higher VPD and AAI). This is supported by evidence indicating that wildfires increase with the increased frequency and intensity of summer heatwaves24, the strengthened solar radiation absorption25,26, and the increased atmospheric water demand and drought conditions27,28 (see Supplementary Fig. 2 for ET0, AAI, and VPD dominance). Major climate modes (e.g., El Niño–Southern Oscillation, and North Atlantic Oscillation) also modulate the occurrence of these weather anomalies29–33 and as such play an important role in governing global patterns of wildfire activities32,34,35. The positive top-down dominance in the rainforests is supported by studies showing that here wildfires are limited to the time window when fuels are dry enough to burn36,37, see Supplementary Fig. 2 for the AAI dominance in the tropical rainforests.

Wildfires tend to be negatively related to bottom-up precursors (78.9% negative, Fig. 1d). Negative patterns prevail in boreal forests, the Brazilian Highlands, southeastern China, Australia, and parts of African savannahs (Supplementary Fig. 5) supporting that vegetation growth limiting factors (such as droughts, heatwaves, and vermin outbreaks) from the previous year or pre-fire season may suppress vegetation growth and maintain fire-prone conditions of fuels38,39. Positive patterns are found mostly in drier and temperate regions (e.g., the western United States, African savannahs, northwestern India, and western Europe, Supplementary Fig. 5), confirming wildfires there are limited by fuels, not flammability. In these regions, wildfires will increase when more fuels are provided (higher FPAR, GPP, NDVI, and EVI) or there is more water supply to increase fuel accumulation by a higher plant growth rate (higher SWDI).

Timelags between precursors and wildfires
For wildfire predictions, the lag between the time when wildfire precursors prevail and a wildfire event is important information because appropriate wildfire-favoring environments often need time to develop. Here, the mean time lags weighted by partial correlations between wildfires and precursors are investigated in detail. Longer time lags are shown in the boreal forests, African and Australian savannahs, the Indian Peninsula, the Indochina Peninsula, and southeastern South America (Fig. 2a). In the other regions, wildfires are a direct result of the precursing conditions (time lags mostly no more than 1 week).

On a global and continental scale, the time lags of top-down precursors are always shorter than bottom-up precursors (Fig. 2b). These short time lags can be explained by the higher variability of top-down conditions and their direct influence on wildfires (not through changing the fuel accumulation). Longer time lags of top-down precursors are limited in Eastern Siberia, the Indian Peninsula, the Indochina Peninsula, eastern South America, and tropical Africa (Supplementary Fig. 6). In this case, one possible reason is that the top-down precursors affect wildfires through their influence on fuel accumulation and availability.

The bottom-up precursors show long time lags, especially in Europe, Asia, North America, and Africa (more than half a year, Fig. 2b). One possible reason could be that the current vegetation status or accumulated fuel is related to water supply and vegetation growth in the pre-wildfire seasons39,40. This is supported by studies showing that spring greening related to global warming can either be positively or negatively related to total biomass41,42, through the carryover effect of current vegetation states on subsequent growth caused by increased photosynthesis or earlier soil moisture depletion38,39. The bottom-up precursors can also come with negligible time lags, mainly in Australia and South America (Fig. 2b and Supplementary Fig. 7), indicating that they can serve as not only fuel accumulation (longer time lag) but also fuel dryness (short time lag) indicators. Note that Fig. 2c shows the dominant time lag distribution in each group, we also investigated the weighted mean time lag distribution (no matter whether the group is dominant or not). Interestingly, the weighted mean case (Supplementary Fig. 11) shows that the time lags of bottom-up precursors are usually short, with a peak of around no more than one week and a second peak of around 60 weeks. We assume that bottom-up precursors serve as fuel accumulation indicators when they dominate wildfires, and fuel dryness indicators when top-down precursors dominate wildfires.

What controls the dominance of wildfire precursors?
To generalize the previous analysis, we evaluated the dominance changes (according to the dominant pixel number) of top-down and bottom-up precursors that are primarily driven by energy, dryness, vegetation, and location (Tmax, VPD, EVI, and latitude, Fig. 3). The dominance of bottom-up precursors peaks when Tmax is around −3°C and 32°C, in regions where fuels are limited or take a longer time to accumulate. When Tmax is between −3°C and 32°C, the top-down precursors gain dominance as fuels may be more readily available. The dominance of top-down precursors decreases when dryness (higher VPD) and sparseness of vegetation (lower EVI) rise. In arid regions, wildfires are limited by fuel accumulation and connectivity. This corroborates the positive relationship between bottom-up precursors and wildfires in dry regions like western North America and African savannahs (Supplementary Fig. 5). When the environment is generally wet (lower VPD) and fuels are not limited (higher EVI), top-down precursors gain dominance, as can be seen in the positive relation between top-down precursors and fire activity (Supplementary Fig. 4). This is supported by the study conducted by Qing et al., 202243 which indicates that flash droughts which promote wildfires caused by heatwave or precipitation deficiency (top-down conditions) are more likely to occur in humid and semi-humid regions.

On a global scale, the top-down precursors are more dominant in the tropical rainforests, mid-latitudes, and boreal forests (eastern Siberia) where the weather is humid, and fuels are abundant or less flammable (Fig. 1b and Fig. 3d). In tropical rainforests, the environmental conditions are not suitable for wildfires unless top-down conditions are extreme44. Therefore, top-down precursors almost synchronize with fire activity in these regions38,45 (Fig. 3e). In the mid-latitudes of Eurasia, the top-down dominance may be caused by sufficient fuels and reliable dry seasons. Moreover, as shown in Supplementary Fig. 15, the fuel type in the mid-latitudes mainly consists of fine fuels, which will dry out in a short time under hot and dry weather conditions46. In North America, the top-down precursors dominate wildfires through, e.g., earlier winter snowpack melting caused by temperature increase, droughts related to summer transpiration increase40,47, and plant growth or photosynthesis changes influenced by VPD through stomatal closure48. The dominance of top-down precursors in eastern Siberia boreal forests is supported by the study conducted by Kim et al., 202049 which shows that in southeastern Siberian boreal forests, the length of the wildfire season is temperature-limited. Moreover, here the changes in top-down conditions may alter thaw depth50 and cause early snow-melting above the frozen soil, but this melted water converts to surface runoff and cannot supply soil and vegetation. This process and the low-pass filtering effect of soils51 (i.e., filtering out high-frequency variations of precipitation and showing longer persistence) may explain the long time lag of top-down precursors (Fig. 3e).

Three peaks of bottom-up dominance show around 60°N, 10°N, and 20°S which corresponds to North American and European boreal forests, and African and Australian savannahs (Fig. 1b and Fig. 3d). Note that wildfires in North American and European boreal forests show bottom-up dominance while in eastern Siberian boreal forests show top-down dominance. This could be explained by the fact that in eastern Siberia, boreal forests consist of fire-resistant species, and wildfires there are prone to low-intensity surface fires, while in North American boreal forests where fire-embracing black spruce forests are dominant, wildfires are prone to high-intensity crown fires which consume more fuels52. A study conducted by Alvarado et al., 202053 indicated that wildfires in Australian savannahs are mainly controlled by fuel load, while in African savannahs they are equally controlled by fuel load and fuel dryness. This may explain the overwhelming bottom-up dominance in Australian savannahs and approximately equal dominance of top-down and bottom-up precursors in African savannahs (Fig. 1b). In African savannahs, evidence also shows that precipitation reduction and cropland expansion reduce fuel accumulation, connectivity, and flammability, and thus prevent the ignition and spread of wildfires54–56, as can be seen in the positive partial correlations of bottom-up precursors in this region (Supplementary Fig. 5).

We further investigated the influence of climate zone and vegetation type on the dominance of wildfire precursors, the results are shown in Fig. 4. The top-down precursors show more dominance in tropical, temperate, and continental zones than in arid zones. This is supported by the top-down dominance in Fig. 3a, b when the climate is cold and humid. In arid zones, including sparse vegetation and shrubland, the dominance of top-down precursors is weaker as wildfires are limited by fuels, which is in line with Fig. 3b (high VPD). In grassland, the top-down precursors show dominance as the seasonal grass dies every year and turns to dead fine fuels, whose dryness quickly responds to changes in top-down precursors46. In cropland where the fuel load is more stable, wildfires are more likely controlled by top-down precursors. The top-down precursors show more dominance in broad-leaf forests than in needle-leaf forests. Needle leaves generally have lower water content and are more flammable than broad leaves, which makes the ignition and spread of wildfires less limited by top-down precursors. In needle-leaf forests, especially needle-leaf evergreen forests, wildfires are coupled with bottom-up precursors as needle leaves decompose slower than broad leaves57. Wildfires in broad-leaf evergreen forests are more affected by top-down precursors. For example, in the Amazon rainforests, the forests have generally large and bulky leaves, which makes them less flammable, and wildfires tend to be surface fires while dead fuels cannot accumulate because of the high decomposition rate58, decoupling the wildfires from bottom-up precursors.

Methods
Datasets
The wildfire indicator used in this study is the burned area (BA) taken from FireCCI5171. FireCCI51 is based on spectral and thermal information from the Moderate Resolution Imaging Spectroradiometer (MODIS) near-infrared band and active wildfire products. It is a global and monthly BA product with two different spatial resolutions (250 m of pixel level and 0.25° of grid level). Here we used the pixel-level data. The main information is included in three layers, i.e., the first fire detection day, the confidence level, and its corresponding land cover. A quality control process was applied to mask low-quality BA pixels whose confidence level is lower than 70%. A logarithmic transformation was applied to transform skewed BA data to a normal Gaussian distribution to make the detected causal relationship reliable72,73.

The top-down precursors include maximum air temperature (Tmax), vapor pressure deficit (VPD), potential evaporation (ET0), wind speed (Wind), and aridity anomaly index (AAI). The bottom-up precursors include the fraction of photosynthetically active radiation (FPAR), gross primary production (GPP), normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and soil water deficit index (SWDI). A detailed description of the precursors - including which products were used, their spatial and temporal resolution, how they were calculated, and how these precursors were selected - can be found in the Supplementary Methods.

All the above-mentioned datasets span from 2003 to 2020. We performed the causal analysis at the ecoregional level considering that top-down weather and bottom-up fuel conditions are relatively similar within the same ecoregion74. We believe that this spatial aggregation is appropriate to generalize findings at a global scale, yet still allows for sufficient spatial variation when analyzing global fire regimes. In addition, we can generate a “look-up table” based on ecoregions that provide basic insights needed to develop a wildfire warning/predicting system for locations where we do not have enough in-situ or field data. In each of the 232 ecoregions (29 climate zones and 8 land cover classes), BA and precursors were resampled to a spatial resolution of 0.25° and averaged (precursors) or summed up (BA) to weekly values. Then, we calculated the ecoregional mean (precursors) or sum (BA) values by averaging or summing up all the pixels located in the ecoregions. Note that even though FireCCI51 is a monthly dataset, it includes a layer recording the first fire detection day. Therefore, we computed the weekly summed BA from this daily information. For the precursors, we calculated the weekly mean from their original temporal scale.

The climate zone data used in this study is the global digital Köppen-Geiger map75 from the University of East Anglia and the German Weather Service representing the climate classification of the period from 1986 to 2010 at the spatial resolution of 5 arc minutes. The vegetation type used in this study is from the land cover layer of FireCCI51.

Causal Analysis
The Peter & Clark Momentary Conditional Independence (PCMCI)20,21 is implemented in this study. It is based on time series analysis which takes lagged variables into account to detect spatiotemporal dependency. It consists of two stages: a Peter & Clark (PC) condition selection stage and a Momentary Conditional Independence (MCI) test stage. In the first stage, for each variable, their relevant conditions that do not pass the iterative independence test are removed based on a Markov set discovery algorithm. In the next stage, a false-positive control was adapted to highly relevant interdependent conditions from the first stage. In applications, two main assumptions of PCMCI are causal sufficiency and time series stationarity. The first assumption can be satisfied by considering as many relevant variables as possible. Normally, causal discovery is more reliable when more variables are included. But when too many irrelevant variables are included, the dimensionality increases, and the effect sizes becomes smaller. The above-mentioned condition selection and condition independence test stages in PCMCI mitigate this causal discovery dilemma21. The second assumption can be approximately satisfied by removing the seasonal cycle and trend in the time series. However, real-world applications can violate this assumption when the seasonal cycle is not fixed like some climate modes such as El Niño-Southern Oscillation. Experiments show that PCMCI is efficient even when this assumption is violated, e.g., the time series is highly autocorrelated or has a nonstationary trend21. We aimed to achieve (a) causal sufficiency by involving a wild range of wildfire precursors, and (b) time series stationarity by detrending and calculating anomalies. We used linear regression to remove the trend over the entire period and calculated anomalies to remove the seasonal cycle and alleviate temporal autocorrelation. The formula for the anomaly calculation can be found in the Supplementary Methods. We used a linear condition independence test, partial correlation, in PCMCI (both the condition selection and condition independence test stages) and set a significance level of 0.05 and a maximum time lag of 156 weeks. In each of the 232 ecoregions, we input wildfire activity indicator (BA), top-down precursors (Tmax, VPD, ET0, Wind, and AAI), and bottom-up precursors (FPAR, GPP, NDVI, EVI, and SWDI) and applied PCMCI to get causal networks showing causal links, directions, strengths, and time lags. In this study, we only focused on causal links and the time lags from wildfire precursors to BA. In each ecoregion, after getting the causal network, only the precursors that show causal links to BA are ranked by causal strength (partial correlation), and the precursor ranked first is called the dominant precursor, and its time lag is called the dominant time lag.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "General comments\nThis study focused on idenfifying the most important local predictors of fires. The authors provided\ncomprehensive analysis on mulfiple aspects of the impacts of fire precursors. I find the results\ninteresfing and helpful. However, several issues should be addressed. I have listed my comments below.\nMajor comments\n• This work focused on the impacts of local predictors on fires. However, the method is partly unclear.\nFor example, for a given pixel with fire data, is the data of predictors also obtained from the same pixel\nor surroundings? As fires may not be limited in one pixel, the selected approach may affect the results\nand conclusions.\n• ENSO is a single useful predictor of fires in many regions (e.g., North America, the tropics, Australia\n(Fang et al., 2021; Fuller & Murphy, 2006)). In fact, ENSO predictability is achievable (Ham et al., 2019)\nand the variafions of most of fire precursors are driven by ENSO (Le & Bae, 2022; Thirumalai et al., 2017).\nHence, it might be too complicated to use mulfiple fire precursors as suggested by this work.\n• Similar to the above concern, I think major climate modes (e.g., ENSO and NAO) can be dominant fire\nprecursors, in case these modes are included in the analyses.\nMinor comments\n• Lines 84-96: The role of ENSO should be menfioned. I find this study (Le et al., 2022) might be relevant\nas it showed that ENSO can be a single predictor of fire over mulfiple regions. In fact, ENSO modulate\nregional atmospheric precursors (e.g., surface temperature (Thirumalai et al., 2017), evaporafion (Le &\nBae, 2020)), hydrologic precursors (e.g., soil moisture (Le & Bae, 2022)) and vegetafion precursors (e.g.,\nGPP (Le et al., 2021)).\n• Lines 225-251: The role of major climate modes should be discussed. For example, ENSO (McPhaden et\nal., 2006) and NAO (Hurrell et al., 2003) modulate the variafions of mulfiple fire predictors and thus may\naffect regional fires.", "reviewer_2": "REVIEWER REPORT\nSUMMARY\nIn this study, the authors used a causal inference approach (Peter & Clark Momentary Condifional\nIndependence, PCMCI) to evaluate the influence of atmospheric, hydrologic, and vegetafion precursors\non fires and their fime lags.\nGENERAL COMMENTS\nMy overall appreciafion is that the study is very interesfing and has the advantage of being performed at\na global scale, but the manuscript has too many flaws and needs a major revision before can be accepted\nfor publicafion. The most important problems are lack of clarity and detail, especially in what concerns\ndata and methods; vague, obvious, redundant, and unnecessary sentences; conceptual and\nmethodological issues. Another problem with this manuscript is that it is not clear what is its\ncontribufion to increasing knowledge about wildfire factors. The authors have to explain which of their\nresults are new/innovafive, i.e., what are the noteworthy results?\nNext, I will address what seems to me to be the main problems with the study/manuscript and then list a\nset of specific quesfions/comments/suggesfions that help explain my overall appreciafion.\nConfidence in the results and conclusions strongly depends on the quanfity and quality of the data as\nwell as the adequacy of the methodology. The descripfion of data and methodologies needs to be\nclarified and detailed. The reader does not always understand what the authors did, which prevents\nthem from perceiving and validafing results and conclusions. In this regard, one important issue is the air\ntemperature (Temp) precursor. The authors do not explain which temperature (minimum, mean,\nmaximum, other) and at what level (surface, 2 m, 10 m, 850 hPa, etc.). Based on the statement that “All\nthe data sets are averaged or summed up to weekly…” maybe we can assume that Temp is some kind of\naverage air temperature. However, it is also common knowledge that the vast majority of wildfires and\nburned areas occur under condifions more extreme than average. The authors somehow acknowledge\nthis fact in the first paragraph of the \"Main\" secfion. Thus, in addifion to the authors having to explain\nwhich temperature they used, if they used the mean temperature, they will have to explain why.\nThe results of such a study strongly depend on the chosen precursors. A bad choice of precursors of\na given type/group leads to equally bad results for that type of precursor. How can the reader be confident\nthat the selected precursors are the best/most suitable? It is also not clear how the authors selected the\nprecursors. Was there a pre-selecfion process? If yes, which one? How did they conclude that four of\neach type would be the proper number?\nIf I understand correctly, this study has serious conceptual (please see specific comments) and\nmethodological flaws that can decisively affect the results and lead the reader to wrong conclusions.\nOne of the most important conceptual/methodological problems is the names and composifion of the\nprecursors’ groups. First, there are misclassified precursors. For example, the group of\nhydrological precursors does not seem to me to be the most suitable, as half of them are not hydrological,\nbut atmospheric (precipitafion and FWI). See examples referred to in specific comments. Secondly, and as a\nconsequence of the first, the groups do not seem to me to be the most suitable. For example, in the case\nof hydrological precursors, it seems to me that it would be more appropriate to consider a group of soil\nprecursors or, possibly, soil moisture precursors. On the one hand, the state and type of vegetafion\ndepend more on soil moisture than on all other hydrological aspects. I conclude that the authors agree\nwith this statement because the group of hydrological precursors integrate measurements of soil\nmoisture and not hydrology (e.g., surface and groundwater, with regard to their mechanical, physical and\nchemical properfies, their geographic distribufion, etc.). On the other hand, precursors should be\nindependent within each group and between groups, which they clearly are not. The variables are all\nrelated to each other in such an obvious way that it is not even necessary to menfion an example.\nThe wildfire research community recognizes and agrees that, for a vegetafion wildfire to occur, the\nfollowing requirements are necessary: (i) fuels/plant biomass, (ii) atmospheric condifions that lead the\nfuels/vegetafion to an adequate state of dryness and, (iii) a source of ignifion. The adequate state of\ndryness of the fuels (live or dead vegetafion) is reached with atmospheric condifions of high\ntemperature and low humidity that lead the vegetafion to hydric and/or thermal stress for some fime\n(depending on the type and state of the vegetafion).\nDespite this common knowledge, the authors decided to reclassify atmospheric factors into atmospheric\nand hydrological. They consider: measures of energy (temperature, radiafion and evaporafion) and call\nthem atmospheric precursors; and, measures of water availability (precipitafion, air and soil humidity),\nwhich are essenfially atmospheric and call them hydrological precursors. It is not possible to conclude\nthat the hydrologic precursors are more or less important than the others if the hydrologic precursors\nare not hydrologic.\nThe authors also do not describe what might be the limitafions and constraints that could affect the\nconclusions of the study.\nWithout a major revision of the manuscript to clarify and correct the problems idenfified in this general\nappreciafion and in the specific comments, it is not worthwhile to analyse other aspects of the\nmanuscript.\nSPECIFIC COMMENTS\nLine 4: In this manuscript, the authors used the concept of fire (a planned, wanted and controlled\ncombusfion), which I believe is not correct; the authors should use the concept of wildfire (an\nunplanned and unwanted combusfion of dead or alive vegetal biomass in a rural or natural area).\nLine 4: the first sentence is completely vague; what role? What services? How?\nLine 30: Is it really possible to \"predict\" wildfires \"reliably and accurately\nLines 31-32: “Fuel availability, usually in the form of vegetafion condifions,…”, fuel availability is not\nequal to vegetafion condifions. Please correct the sentence.\nLine 36: I agree that the FWI system is used widely. However, since it was developed for Canada, it must\nbe calibrated before can be used in any other locafion. How did you calibrate the FWI indices in order to\nbe able to use them globally?\nLine 46: I suppose that the black circle character is a typo/not necessary.\nLine 46: you menfioned that you use air temperature (Temp). However, you do not explain what air\ntemperature is; the minimum, mean, maximum or other air temperature? Air temperature at what\nlevel?\nLine 48: can you explain why did you classify Precipitafion, which can be defined as the fall of water,\nin any form (rain, snow, hail, hail, etc.), from the atmosphere to the earth's surface, as a hydrologic and not\nan atmospheric precursor?\nLines 48-49: Please explain why you classify the fire WEATHER index (FWI) as a hydrologic and not an\natmospheric precursor. Please note that the Canadian Forest Fire Weather Index (FWI) System consists of\nsix components that account for the effects of fuel moisture and WEATHER condifions on fire behaviour,\ncomputed with noon air temperature, air relafive humidity and wind speed, and 24-hour accumulated\nprecipitafion.\nPlease quanfify and explain how these misclassificafions impact your findings.\nLine 57 (Fig. 1a): Please explain why the sum of the parfial correlafions of all 12 fire precursors per\necoregion ranges between 0 and 2.\nLines 57-61:\n“For all the 232 ecoregions, the dominant area fracfion of the precursor (Fig. S1) shows that VPD (47.6%)\nis the most important atmospheric precursor followed by Rad, Temp, and ET0 (23.7%, 18.1%, 10.6%,\nrespecfively)”. What is the meaning of these results? Does this mean that VPD is the most important\nprecursor in 47.6% of the 232 ecoregions, analysed independently or jointly?\nIf I understand correctly, the sum of the percentages of the four precursors of each type is 100%. Does\nthis mean that you apply PCMCI independently for the precursors of each type/group (for all the\necoregions)? Please clarify.\nLines 72-73: How did you compute these percentages? Did you use the number of ecoregions, the\narea, or what?\nLine 83 and 91: this is only due to the precursors you selected and how you classify them.\nLine 87: another black circle!\nLines 87-89: An obvious and unnecessary sentence. Is there any region where the atmospheric\nprecursors do not play an important role?\nLine1 91-92: “Fires tend to be negafively related to hydrologic precursors (72.6%), and less frequently\nthis relafionship is posifive (27.4%), see Fig. 1d”; the part “, and less frequently this relafionship is\nposifive (27.4%),” of the sentence is completely unnecessary; the reader can easily compute 100-\n72.6=27.4.\nLines 92-94: another obvious and unnecessary sentence; it is obvious that drought and precipitafion\ndeficits may provide opfimal condifions for wildfires while the opposite, a “high water supply” can\nprevent fires.\nLines 94-96: What “dominant posifive relafionship”\nLine 110: I am confused. If “All the data sets are averaged or summed up to weekly” (line 287), how can\nyou have assessed the parfial correlafion for fime lags shorter than 1 week?\nLines 112-113: “…, which means in such regions fires are ignited shortly after (within a few days) the\nchanges in the fire precursors.” another obvious and unnecessary sentence.\nLines 118-119: what evidence do you have for such a statement? That should not be the reason, at\nleast everywhere.\nLines 125-127: this does not seem correct/accurate; the tropics are a very large region; it is possible to\nbe much more accurate. The explanafion does not also seem correct. Please note that 30 weeks is less\nthan a year.\nLine 140 and 154: area of what?\nLine 154: another black circle.\nLine 165: another black circle.\nLine 196: in the Köppen-Geiger climate classificafion, the B type of climate is arid, not dry. Please correct.\nLines 254-270 (data):\nApparently, you used monthly fire data can the reader assume that the fime series of all other variables\nare also monthly? If not (line 287), how did you proceed to analyse data with different temporal\nsampling?\nDatasets do not have the same spafial resolufion; how did you process the data with different spafial\nresolufions?\nLines 262-263. Why did you use temperature, radiafion, and precipitafion of the Global Land Data\nAssimilafion System (GLDAS) but ET0, VPD, FWI and AAI from ERA5? Why did you not use all weather\ndata from the same source, for example, ERA5, which provides you with not only global but also\nconsistent (homogeneous) data?\nLines 271-285: the reader may not have access to all publicafions cited in the manuscript; this is why the\nmethodology must be, eventually briefly but clearly described, which is not the case. In addifion, the\ndescripfion of how AAI was computed is unclear; for example, “For each year, we calculated the\ndifference between AI at a certain period and the normal AI for the corresponding period to calculate\nthe anomaly, then the AAI was obtained.”\nLines 286-305 (Data Processing Procedures):\nyou menfioned that you use monthly fire data (line 256 “…It is a global and monthly BA product”) and\n“All the data sets are averaged or summed up to weekly values…”. Please explain this apparent\ncontradicfion.\nSo, you produce weekly fime series for each of the 28 climate zones (from Köppen-Geiger) and 8 land\ncover classes (from ESA CCI). The quesfions are:\nWhat was the original temporal resolufion of the downloaded data?\nHow did you proceed to obtain the fime series for each of the 232 ecoregions 28 (or 29) climate zones\nand 8 vegetafion types?\nLine 294: Please clarify what is a “… PC condifion selecfion …”; all acronyms must be clearly defined.\nLines 294-295: Please clarify what is “…an MCI condifional independence test …”; all acronyms must be\nclearly defined.\nLine 295: “In the first stage, irrelevant condifions are removed”; what is an irrelevant condifion?\nLines 296-298: “Two main assumpfions of PCMCI are fime-series stafionarity and causal sufficiency. The\nfirst assumpfion can be safisfied by calculafing anomalies and de-trending the fime series.” Once again,\nthe authors do not adequately explain what leads to doubt. First, obviously, it is not by calculafing\nanomalies that the series becomes stafionary; just think that the trend does not disappear. Second, you\ndon't explain what trend you remove and how. Did you just remove the trend over the enfire study\nperiod? Did you check and correct the seasonality? This whole procedure must be properly explained."}, "author_response": "Reviewer #1 (Remarks to the Author):\nGeneral comments\nThis study focused on identifying the most important local predictors of fires. The authors provided\ncomprehensive analysis on multiple aspects of the impacts of fire precursors. I find the results\ninteresting and helpful. However, several issues should be addressed. I have listed my comments\nbelow.\nResponse: We appreciate the constructive comments and suggestions provided by the reviewer to\nimprove the quality of our manuscript. Our responses to the reviewer’s major and specific comments\nare given below.\nMajor comments\nMajor comment #1\n• This work focused on the impacts of local predictors on fires. However, the method is partly\nunclear. For example, for a given pixel with fire data, is the data of predictors also obtained from\nthe same pixel or surroundings? As fires may not be limited in one pixel, the selected approach may\naffect the results and conclusions.\nResponse: We appreciate the reviewer’s careful review and revised the Methods part to clarify how\nwe processed the data. Since not the entire Earth surface has experienced wildfires in the past\ndecades, we performed the analysis based on 232 ecoregions. We, therefore, implemented the\nfollowing approach: We first resampled all the data to a spatial resolution of 0.25° and calculated\nthe weekly mean (wildfire precursors) or sum (burned area) values. Then, we calculated the\necoregion mean or sum values by averaging or summing up all the pixels located in the ecoregions,\ni.e., the following analysis is purely based on the ecoregion level, not the pixel level. The revised\nMethods are as follows:\nThe wildfire indicator used in this study is the BA taken from FireCCI5173. FireCCI51 is based\non spectral and thermal information from the Moderate Resolution Imaging Spectroradiometer\n(MODIS) near-infrared (NIR) band and active wildfire products. It is a global and monthly BA\nproduct with two different spatial resolutions (250m of pixel level and 0.25° of grid level). Here we\nused the pixel-level data. The main information is included in three layers, i.e., the first fire detection\nday, the confidence level, and its corresponding land cover. A quality control process was applied\nto mask low-quality BA pixels whose confidence level is lower than 70%. A logarithmic\ntransformation was applied to transform skewed BA data to a normal Gaussian distribution to make\nthe detected causal relationship reliable74, 75.\nThe top-down precursors include maximum air temperature (Tmax), vapor pressure deficit\n(VPD), potential evaporation (ET0), wind speed (Wind), and aridity anomaly index (AAI). The\nbottom-up precursors include the fraction of photosynthetically active radiation (FPAR), gross\nprimary production (GPP), normalized difference vegetation index (NDVI), enhanced vegetation\nindex (EVI), and soil water deficit index (SWDI). A detailed description of the precursors - including\nwhich products were used, their spatial and temporal resolution, how they were calculated, and how\nthese precursors were selected - can be found in the Supplementary Materials.\nAll the above-mentioned data sets span from 2003 to 2020. Since not the entire world surface\nhas experienced wildfires in the past decades, we performed the analysis based on ecoregions\ninstead of at a pixel level. In each of the 232 ecoregions (29 climate zones and 8 land cover classes),\nBA and precursors were resampled to a spatial resolution of 0.25° and averaged (precursors) or\nsummed up (BA) to weekly values. Then, we calculated the ecoregional mean (precursors) or sum\n(BA) values by averaging or summing up all the pixels located in the ecoregions. Note that even\nthough FireCCI51 is a monthly dataset, it includes a layer recording the first fire detection day.\nTherefore, we computed the weekly summed BA from this daily information. For the precursors, we\ncalculated the weekly mean from their original temporal scale.\nThe climate zone data used in this study is the global digital Köppen-Geiger map76 from the\nUniversity of East Anglia and the German Weather Service representing the climate classification\nof the period from 1986 to 2010 at the spatial resolution of 5 arc minutes. The vegetation type used\nin this study is from the land cover layer of FireCCI51. (Line 286-313)\nMajor comment #2\n• ENSO is a single useful predictor of fires in many regions (e.g., North America, the tropics,\nAustralia (Fang et al., 2021; Fuller & Murphy, 2006)). In fact, ENSO predictability is achievable\n(Ham et al., 2019) and the variations of most of fire precursors are driven by ENSO (Le & Bae,\n2022; Thirumalai et al., 2017). Hence, it might be too complicated to use multiple fire precursors as\nsuggested by this work.\nResponse: We appreciate the reviewer’s insights about the dominance of major climate modes, like\nENSO, on wildfires, and we agree. Part of our author team is involved in an ongoing study, in which\nthe influence of major climate modes on burned area is investigated. Below are all the climate mode\nindices we included in the ongoing study:\nClimate Mode Climate Mode Index\nAntarctic Oscillation AAO index (daily)\nArctic Oscillation AO index (daily)\nPolar Eurasia Pattern PEA index (monthly)\nNorth Atlantic Oscillation NAO index (daily)\nEast Atlantic West Russia Pattern EAWR index (monthly)\nTropical Southern Atlantic Dipole TSA index (monthly)\nTropical Northern Atlantic Dipole TNA index (monthly)\nIndian Ocean Dipole IOD index (monthly)\nWest Pacific Pattern WP index (monthly)\nPacific–North American Pattern PNA index (daily)\nEl Niño Southern Oscillation ENSO index (daily)\nThe majority of climate mode indices are available at a monthly temporal resolution. In our current\nstudy, we are interested in the influences of precursors on a weekly temporal resolution, and hence\nit is not feasible to integrate the climate indices in this framework. A weekly temporal resolution is\ndesirable for our study because fire activity can fluctuate on a daily scale, and much of this\ninformation would be lost at a monthly resolution.\nAlthough we did not involve climate modes in this study, our results and findings are influential and\ncomplementary to earlier work that investigated relationships between climate modes and fire\nactivity. Climate modes act upon wildfires through their influence on the top-down and bottom-up\npredictors that are here investigated (e.g., precipitation, temperature, GPP).\nIn conclusion, our current precursor study has its own focus. This causal study focuses on what\ndominates wildfires, where, and the time lags between wildfire precursors and wildfires. For the\nabovementioned reasons, we believe that the inclusion of the climate modes would distract from the\nmain storyline of this manuscript and would go beyond its scope.\nWe provide further discussion on relationships between our study and earlier studies focusing on\nrelationships between climate modes and wildfires as suggested by the reviewer in Minor comment\n#1 and Minor comment #2 (see below).\nMajor comment #3\n• Similar to the above concern, I think major climate modes (e.g., ENSO and NAO) can be dominant\nfire precursors, in case these modes are included in the analyses.\nResponse: Thanks for the comment. As we responded in Major comment #2, the inclusion of climate\nmodes will not change the dominance of top-down or bottom-up precursors, and we consider this\ncomplementary information. We have appended a discussion on how major climate modes may\ninfluence precursors and fire activity, see Minor comment #1 and Minor comment #2.\nMinor comments\nMinor comment #1\n• Lines 84-96: The role of ENSO should be mentioned. I find this study (Le et al., 2022) might be\nrelevant as it showed that ENSO can be a single predictor of fire over multiple regions. In fact,\nENSO modulate regional atmospheric precursors (e.g., surface temperature (Thirumalai et al., 2017),\nevaporation (Le & Bae, 2020)), hydrologic precursors (e.g., soil moisture (Le & Bae, 2022)) and\nvegetation precursors (e.g., GPP (Le et al., 2021)).\nResponse: Thanks for the reviewer’s constructive suggestion. We addressed the role of major\nclimate modes on wildfires as follows:\nWildfires are positively related to top-down precursors globally (91.6% positive, Fig. 1d). This\npositive relationship indicates that in flammability-limited regions, BA will increase when more\nenergy is supplied (higher Tmax and ET0) or the environment is drier (higher VPD and AAI). This\nis supported by evidence indicating that wildfires increase with the increased frequency and\nintensity of summer heatwaves25, the strengthened solar radiation absorption26, 27, and the increased\natmospheric water demand and drought conditions28, 29 (see Fig. S2 for ET0, AAI, and VPD\ndominance). Major climate modes (e.g., El Niño–Southern Oscillation, and North Atlantic\nOscillation) also modulate the occurrence of these weather anomalies30, 31, 32, 33, 34 and as such play\nan important role in governing global patterns of wildfire activities33, 35, 36. The top-down dominance\nin the rainforest is supported by studies showing that here wildfires are limited to the time window\nwhen fuels are dry enough to burn37, 38, see Fig. S2 for the AAI dominance. (Line 86-97)\nReference:\n30. Le T, Bae DH. Causal Impacts of El Niño–Southern Oscillation on Global Soil Moisture Over\nthe Period 2015–2100. Earth's Future 2022, 10(3).\n31. Le T, Bae D-H. Response of global evaporation to major climate modes in historical and future\nCoupled Model Intercomparison Project Phase 5 simulations. Hydrology and Earth System\nSciences 2020, 24(3): 1131-1143.\n32. Le T, Ha KJ, Bae DH. Increasing Causal Effects of El Niño–Southern Oscillation on the Future\nCarbon Cycle of Terrestrial Ecosystems. Geophysical Research Letters 2021, 48(24).\n33. Le T, Kim SH, Bae DH. Decreasing causal impacts of El Nino-Southern Oscillation on future\nfire activities. Sci Total Environ 2022, 826: 154031.\n34. Thirumalai K, DiNezio PN, Okumura Y, Deser C. Extreme temperatures in Southeast Asia\ncaused by El Nino and worsened by global warming. Nat Commun 2017, 8: 15531.\n35. Fang K, Yao Q, Guo Z, Zheng B, Du J, Qi F, et al. ENSO modulates wildfire activity in China.\nNat Commun 2021, 12(1): 1764.\n36. fuller DO, Murphy K. The Enso-Fire Dynamic in Insular Southeast Asia. Climatic Change\n2006, 74(4): 435-455.\nMinor comment #2\n• Lines 225-251: The role of major climate modes should be discussed. For example, ENSO\n(McPhaden et al., 2006) and NAO (Hurrell et al., 2003) modulate the variations of multiple fire\npredictors and thus may affect regional fires.\nResponse: We appreciate the reviewer’s constructive comment. As explained in our response to\nMajor comment #2, we find that our study on precursors influences global fire activity\ncomplementary to earlier work that investigated relationships between major climate modes and\nfire activity. In our revision, we now better connect our work with these earlier studies and discuss\nas a limitation the non-explicit consideration of the influence of climate modes:\nThere are three limitations of this study that could be addressed further. First, although\nbenefiting from the big data era, the number of precursors included in this study is still limited.\nMajor climate modes (e.g., El Niño–Southern Oscillation, and North Atlantic Oscillation) modulate\nregional top-down and bottom-up conditions and dominant wildfires69, 70. Therefore, wildfire\nprediction could benefit from the achievable predictability of El Niño–Southern Oscillation71 and\nother major climate modes. Human behavior (e.g., ignition, cropland expansion, and fire\nsuppression) is an important wildfire impact factor that may alter causal relationships. However,\nlimited by the availability of high temporal resolution climate mode and human behavior indices,\nwe currently cannot conduct such an analysis. Second, a better wildfire precursor grouping system\nmay be beneficial. While we divided the precursors into top-down and bottom-up groups which are\nconceptually clear72, sometimes, the top-down precursors dominate wildfires through their influence\non fuel accumulation that is deduced from their long time lags, making the causal inference complex.\nThird, we assumed the causal relationship to be constant throughout the whole study period.\nHowever, extreme events or human behavior can alter fire regimes and potentially change causal\nrelationships. (Line 253-266)\nReference:\n69. Hurrell JW, Kushnir Y, Ottersen G, Visbeck M. An Overview of the North Atlantic\nOscillation. The North Atlantic Oscillation: Climatic Significance and Environmental Impact, 2003, pp 1-35.\n70. McPhaden MJ, Zebiak SE, Glantz MH. ENSO as an integrating concept in earth science.\nScience 2006, 314(5806): 1740-1745.\n71. Ham YG, Kim JH, Luo JJ. Deep learning for multi-year ENSO forecasts. Nature 2019,\n573(7775): 568-572.\nReferences\nFang, K., Yao, Q., Guo, Z., Zheng, B., Du, J., Qi, F., et al. (2021). ENSO modulates wildfire activity\nin China. Nature Communications, 12(1), 1764. https://doi.org/10.1038/s41467-021-21988-6\nFuller, D. O., & Murphy, K. (2006). The Enso-Fire Dynamic in Insular Southeast Asia. Climatic\nChange, 74(4), 435–455. https://doi.org/10.1007/s10584-006-0432-5\nHam, Y. G., Kim, J. H., & Luo, J. J. (2019). Deep learning for multi-year ENSO forecasts. Nature,\n573(7775), 568–572. https://doi.org/10.1038/s41586-019-1559-7\nHurrell, J. W., Kushnir, Y., Ottersen, G., & Visbeck, M. (2003). An overview of the North Atlantic\nOscillation. In Geophysical Monograph American Geophysical Union (pp. 1–35). American\nGeophysical Union. https://doi.org/10.1029/134GM01\nLe, T., & Bae, D.-H. (2020). Response of global evaporation to major climate modes in historical\nand future Coupled Model Intercomparison Project Phase 5 simulations. Hydrology and Earth\nSystem Sciences, 24(3), 1131–1143. https://doi.org/10.5194/hess-24-1131-2020\nLe, T., & Bae, D. (2022). Causal Impacts of El Niño–Southern Oscillation on Global Soil Moisture\nOver the Period 2015–2100. Earth’s Future, 10(3). https://doi.org/10.1029/2021EF002522\nLe, T., Ha, K., & Bae, D. (2021). Increasing Causal Effects of El Niño–Southern Oscillation on the\nFuture Carbon Cycle of Terrestrial Ecosystems. Geophysical Research Letters, 48(24).\nhttps://doi.org/10.1029/2021GL095804\nLe, T., Kim, S., & Bae, D. (2022). Decreasing causal impacts of El Niño–Southern Oscillation on\nfuture fire activities. Science of The Total Environment, 826, 154031.\nhttps://doi.org/10.1016/j.scitotenv.2022.154031\nMcPhaden, M. J., Zebiak, S. E., & Glantz, M. H. (2006). ENSO as an integrating concept in earth\nscience. Science (New York, N.Y.), 314(December), 1740–1745.\nhttps://doi.org/10.1126/science.1132588\nThirumalai, K., DInezio, P. N., Okumura, Y., & Deser, C. (2017). Extreme temperatures in Southeast\nAsia caused by El Ninõ and worsened by global warming. Nature Communications, 8, 1–8.\nhttps://doi.org/10.1038/ncomms15531\nReviewer #2 (Remarks to the Author):\nREVIEWER REPORT\nSUMMARY\nIn this study, the authors used a causal inference approach (Peter & Clark Momentary Conditional\nIndependence, PCMCI) to evaluate the influence of atmospheric, hydrologic, and vegetation\nprecursors on fires and their time lags.\nGENERAL COMMENTS\nGENERAL COMMENT #1:\nMy overall appreciation is that the study is very interesting and has the advantage of being\nperformed at a global scale, but the manuscript has too many flaws and needs a major revision\nbefore can be accepted for publication. The most important problems are lack of clarity and detail,\nespecially in what concerns data and methods; vague, obvious, redundant, and unnecessary\nsentences; conceptual and methodological issues. Another problem with this manuscript is that it is\nnot clear what is its contribution to increasing knowledge about wildfire factors. The authors have\nto explain which of their results are new/innovative, i.e., what are the noteworthy results?\nNext, I will address what seems to me to be the main problems with the study/manuscript and then\nlist a set of specific questions/comments/suggestions that help explain my overall appreciation.\nResponse: We appreciate the reviewer’s positive comment on this topic and regret our unclarity\nwith the data, methods, and significance. We believe that our manuscript now has been improved\nsignificantly according to the reviewer’s comments and suggestions. Our responses to the reviewer’s\ngeneral and specific comments are given below.\nWe also highlighted the noteworthy results in the Implications section:\nIn conclusion, this is the first globally consistent study partitioning top-down weather and\nbottom-up fuel precursors of wildfire activity. On a global scale, top-down weather precursors\ndominate in 73.3% of regions where causal relationships are detected. In the remaining 26.7% of\nregions, bottom-up fuel precursors are dominant, which coincides with North American and\nEuropean boreal forests, and African and Australian savannahs. In these regions, fuel management\npractices may be the most efficient. (Line 267-271)\nGENERAL COMMENT #2:\nConfidence in the results and conclusions strongly depends on the quantity and quality of the data\nas well as the adequacy of the methodology. The description of data and methodologies needs to be\nclarified and detailed. The reader does not always understand what the authors did, which prevents\nthem from perceiving and validating results and conclusions. In this regard, one important issue is\nthe air temperature (Temp) precursor. The authors do not explain which temperature (minimum,\nmean, maximum, other) and at what level (surface, 2 m, 10 m, 850 hPa, etc.). Based on the statement\nthat “All the data sets are averaged or summed up to weekly…” maybe we can assume that Temp is\nsome kind of average air temperature. However, it is also common knowledge that the vast majority\nof wildfires and burned areas occur under conditions more extreme than average. The authors\nsomehow acknowledge this fact in the first paragraph of the \"Main\" section. Thus, in addition to the\nauthors having to explain which temperature they used, if they used the mean temperature, they will\nhave to explain why.\nResponse: We appreciate the reviewer’s constructive comment to clarify the Data section. We used\ndaily mean 2m air temperature in the earlier submitted version. Thanks for the reviewer’s\nsuggestion about the maximum temperature. We agree that wildfires are more related to the\nmaximum temperature. So, in this revised version, we replaced the daily mean 2m air temperature\nwith the daily maximum 2m air temperature. To make clear which variables/precursors were used\nin this study, we summarized this information in a new Supplementary Table (Table S1):\nTable S1. All the indices involved in the causal analysis. The indices marked with ‘*’ were removed from this study according to\nthe data selection result.\nGroup/Class Index/Precursor Calculated? Product Spatial Scale Temporal Scale\nFire Burned Area (BA) - Fire_CCI 250 m Monthly\nPotential Evaporation (ET0) - ERA5 0.25 degree Daily\nAridity Anomaly Index (AAI) √ (ERA5) 0.25 degree Daily\n2m Maximum Air Temperature (Tmax) - ERA5 0.25 degree Daily\nTop-down Surface Net Solar Radiation (Rad)* - ERA5 0.25 degree Daily\nVapor Pressure Deficit (VPD) √ (ERA5) 0.25 degree Daily\nTotal Precipitation (Prec)* - ERA5 0.25 degree Daily\n10m Wind Speed (Wind) - ERA5 0.25 degree Daily\nSoil Water Deficit Index (SWDI) √ (ERA5) 0.25 degree Daily\nFraction of Photosynthetically Active Radiation (FPAR) - MCD15A3H 500 m 4-day\nBottom-up Normalized Difference Vegetation Index (NDVI) - MOD13C1 0.05 degree 16-day\nEnhanced Vegetation Index (EVI) - MOD13C1 0.05 degree 16-day\nGross Primary Production (GPP) - MOD17A2HGF 500 m 8-day\nGENERAL COMMENT #3:\nThe results of such a study strongly depend on the chosen precursors. A bad choice of precursors of\na given type/group leads to equally bad results for that type of precursor. How can the reader be\nconfident that the selected precursors are the best/most suitable? It is also not clear how the authors\nselected the precursors. Was there a pre-selection process? If yes, which one? How did they conclude\nthat four of each type would be the proper number?\nResponse: Thanks for the reviewer’s comment. We performed an extensive study to evaluate the\nindividual precursor contributions. We added a new section in the Supplementary Material to\nexplain how we selected precursors:\nData Selection\nBenefiting from the big data era (using remote sensing and reanalysis data), we can now perform a comprehensive wildfire\ncausation study. We started by collecting all the data listed in Table S1. To make the comparison between the Top-down and\nBottom-up groups more objective and transparent, we used a variable clustering method (“varclushi” package in Python) to execute\nthe precursor selection.\nVariable clustering is based on the Principal Component Analysis (PCA) algorithm. Firstly, all the input variables are started\nin one cluster, and a PCA is done for all the variables in this cluster. Secondly, the Eigenvalues (variance explained by each PC for\nall the variables) of the principal components are checked. The cluster is spilt if the second Eigenvalue is larger than a specified\nthreshold (usually set as 0.7 or 1). This cluster-splitting process is iterated until all the second Eigenvalues in all clusters are lower\nthan the specified threshold and the clusters cannot be further spilt. Thirdly, in each cluster, the representative variables having the\nhighest correlation with their own cluster and the lowest correlation with other clusters are selected according to the formula below:\n1−(cid:1844)(cid:2870)\n(cid:1844)(cid:1853)(cid:1872)(cid:1861)(cid:1867)(cid:3404) (cid:3042)(cid:3050)(cid:3041)\n1−(cid:1844)(cid:2870)\n(cid:3041)(cid:3032)(cid:3028)(cid:3045)(cid:3032)(cid:3046)(cid:3047)\nwhere (cid:1844)(cid:2870) is the coefficient of determination ((cid:1844)(cid:2870)) of the variable with its own cluster, and the (cid:1844)(cid:2870) is the (cid:1844)(cid:2870) of the variable\n(cid:3042)(cid:3050)(cid:3041) (cid:3041)(cid:3032)(cid:3028)(cid:3045)(cid:3032)(cid:3046)(cid:3047)\nwith the nearest cluster. The one with the lowest (cid:1844)(cid:1853)(cid:1872)(cid:1861)(cid:1867) is selected in each cluster.\nFig S1. Variable clustering result.\nWe used partial correlation in PCMCI to detect casual relationships, since it can eliminate the interactions between variables.\nAs a consequence, all variables can be included in the PCMCI analysis. Considering that there are only five bottom-up precursors\nwhile seven top-down precursors, we decided to select the top five variables from the top-down group in order to prevent any\nartificial inflation of the importance of top-down variables. We applied variable clustering for all the 232 ecoregions and recorded\nthe representative variables in each ecoregion. For each variable, we counted globally the pixels where it is representative, the\nresult is shown in Fig S1. Therefore, according to the number of representative pixels, the variables that are finally selected were\nAAI, Wind, ET0, VPD, and Tmax in the Top-down group and SWDI, EVI, NDVI, GPP, and FPAR in the Bottom-up group.\n(Supplementary Material Line 31-54)\nGENERAL COMMENT #4:\nIf I understand correctly, this study has serious conceptual (please see specific comments) and\nmethodological flaws that can decisively affect the results and lead the reader to wrong conclusions.\nOne of the most important conceptual/methodological problems is the names and composition of\nthe precursors’ groups. First, there are misclassified precursors. For example, the group of\nhydrological precursors does not seem to me to be the most suitable, as half of them are not\nhydrological, but atmospheric (precipitation and FWI). See examples referred to in specific\ncomments. Secondly, and as a consequence of the first, the groups do not seem to me to be the most\nsuitable. For example, in the case of hydrological precursors, it seems to me that it would be more\nappropriate to consider a group of soil precursors or, possibly, soil moisture precursors. On the one\nhand, the state and type of vegetation depend more on soil moisture than on all other hydrological\naspects. I conclude that the authors agree with this statement because the group of hydrological\nprecursors integrate measurements of soil moisture and not hydrology (e.g., surface and\ngroundwater, with regard to their mechanical, physical and chemical properties, their geographic\ndistribution, etc.). On the other hand, precursors should be independent within each group and\nbetween groups, which they clearly are not. The variables are all related to each other in such an\nobvious way that it is not even necessary to mention an example.\nResponse: We appreciate the reviewer’s constructive comment, and we agree that such a grouping\nsystem could be misleading. In the revised version, we changed the grouping system. Now we\ncategorize precursors into top-down and bottom-up groups, which is a widely used terminology in\nwildfire studies1,2. We categorized Potential Evaporation (ET0), Aridity Anomaly Index (AAI), 2m\nMaximum Air Temperature (Tmax), Surface Net Solar Radiation (Rad), Vapor Pressure Deficit\n(VPD), Total Precipitation (Prec), and 10m Wind Speed (Wind) as top-down group, and categorized\nSoil Water Deficit Index (SWDI), Fraction of Photosynthetically Active Radiation (FPAR),\nNormalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Gross\nPrimary Production (GPP) as bottom-up group. We believe this new grouping system is\nconceptually sound and in correspondence with existing fire literature.\nWe agree that it would be beneficial to ensure mutually independent. However, this would be a very\nstringent requirement. To eliminate the influence of interactions, we used partial correlation to\ndetect casual relationships. Partial correlation quantifies the correlation between two variables\nwhen conditioned on other variables. After removing the effect of other variables, what remains is\nthe partial correlation between the two target variables. Therefore, we can include all the wildfire\nprecursors in this causation study, even if they are dependent; this is in fact the justification for and\nadvantage of using PCMCI3. To maintain a balance between the Top-down group and the Bottom-\nup group, we used variable clustering to find the representative precursors globally. By doing this,\nwe cannot eliminate all interactions among precursors but try to keep them as independent as\npossible. By using the conceptual grouping system, variable clustering, and partial correlation, the\napproach allows attributing causation to the correct precursors even if they are interdependent.\nReference:\n1. Walker XJ, Rogers BM, Veraverbeke S, Johnstone JF, Baltzer JL, Barrett K, et al. Fuel\navailability not fire weather controls boreal wildfire severity and carbon emissions. Nature Climate\nChange 2020, 10(12): 1130-1136.\n2. Gill L, Taylor AH. Top-Down and Bottom-Up Controls on Fire Regimes Along an Elevational\nGradient on the East Slope of the Sierra Nevada, California, USA. Fire Ecology 2009, 5(3): 57-75.\n3. Runge J, Nowack P, Kretschmer M, Flaxman S, Sejdinovic D. Detecting and quantifying causal\nassociations in large nonlinear time series datasets. Sci Adv 2019, 5(11): eaau4996.\nGENERAL COMMENT #5:\nThe wildfire research community recognizes and agrees that, for a vegetation wildfire to occur, the\nfollowing requirements are necessary: (i) fuels/plant biomass, (ii) atmospheric conditions that lead\nthe fuels/vegetation to an adequate state of dryness and, (iii) a source of ignition. The adequate state\nof dryness of the fuels (live or dead vegetation) is reached with atmospheric conditions of high\ntemperature and low humidity that lead the vegetation to hydric and/or thermal stress for some time\n(depending on the type and state of the vegetation).\nDespite this common knowledge, the authors decided to reclassify atmospheric factors into atmospheric\nand hydrological. They consider: measures of energy (temperature, radiation, and evaporation)\nand call them atmospheric precursors; and, measures of water availability (precipitation, air, and soil\nhumidity), which are essentially atmospheric and call them hydrological precursors. It is not possible\nto conclude that the hydrologic precursors are more or less important than the others if the hydrologic\nprecursors are not hydrologic.\nThe authors also do not describe what might be the limitations and constraints that could affect the\nconclusions of the study.\nWithout a major revision of the manuscript to clarify and correct the problems identified in this\ngeneral appreciation and in the specific comments, it is not worthwhile to analyze other aspects of\nthe manuscript.\nResponse: Thanks for the reviewer’s comment, we changed the grouping system as explained above.\nIn the new system, we classify atmospheric conditions (including energy and humidity) as the top-\ndown group, and fuel/plant conditions as the bottom-up group.\nWe also discussed the limitations of this study in the Implication part as follows:\nThere are three limitations of this study that could be addressed further. First, although\nbenefiting from the big data era, the number of precursors included in this study is still limited.\nMajor climate modes (e.g., El Niño–Southern Oscillation, and North Atlantic Oscillation) modulate\nregional top-down and bottom-up conditions and dominant wildfires69, 70. Therefore, wildfire\nprediction could benefit from the achievable predictability of El Niño–Southern Oscillation71 and\nother major climate modes. Human behavior (e.g., ignition, cropland expansion, and fire\nsuppression) is an important wildfire impact factor that may alter causal relationships. However,\nlimited by the availability of high temporal resolution climate mode and human behavior indices,\nwe currently cannot conduct such an analysis. Second, a better wildfire precursor grouping system\nmay be beneficial. While we divided the precursors into top-down and bottom-up groups which are\nconceptually clear72, sometimes, the top-down precursors dominate wildfires through their influence\non fuel accumulation that is deduced from their long time lags, making the causal inference complex.\nThird, we assumed the causal relationship to be constant throughout the whole study period.\nHowever, extreme events or human behavior can alter fire regimes and potentially change causal\nrelationships. (Line 253-266)\nSPECIFIC COMMENTS\nSPECIFIC COMMENT #1:\nLine 4: In this manuscript, the authors used the concept of fire (a planned, wanted, and controlled\ncombustion), which I believe is not correct; the authors should use the concept of wildfire (an\nunplanned and unwanted combustion of dead or alive vegetal biomass in a rural or natural area).\nResponse: We appreciate the accuracy of the word/term used by the reviewer and we have replaced\nfire with wildfire in the revision.\nSPECIFIC COMMENT #2:\nLine 4: the first sentence is completely vague; what role? What services? How?\nResponse: We deleted this sentence in the abstract.\nSPECIFIC COMMENT #3:\nLine 30: Is it really possible to \"predict\" wildfires \"reliably and accurately?\nResponse: Thanks for the reviewer’s comment. We have rephrased this sentence to:\nBetter understanding and management of wildfires, therefore, is necessary to mitigate their negative\nimpacts on human livelihood and the Earth`s natural system. (Line 28-29)\nSPECIFIC COMMENT #4:\nLines 31-32: “Fuel availability, usually in the form of vegetation conditions,…”, fuel availability is\nnot equal to vegetation conditions. Please correct the sentence.\nResponse: We appreciate the reviewer’s careful review. We changed it to:\nThe availability of fuel is highly influenced by previous and current vegetation status and is a\nprecondition for wildfires, yet extensive burning only occurs when fuels are dry and weather\nconditions are conducive to wildfire spread18. (Line 29-31)\nSPECIFIC COMMENT #5:\nLine 36: I agree that the FWI system is used widely. However, since it was developed for Canada,\nit must be calibrated before can be used in any other location. How did you calibrate the FWI indices\nin order to be able to use them globally?\nResponse: Thanks for the reviewer’s comment, we totally agree that the global use of the FWI\nsystem should be done in a careful manner. Its inputs (noon air temperature, air relative humidity,\n24-hour accumulated precipitation, and wind speed) have become part of the Top-down group of\nprecursors in the revision, while other direct measurements have been used to characterize bottom-\nup fuel status. To avoid this kind of conceptual problem, we have no longer included the FWI as a\nprecursor in the revised analysis.\nSPECIFIC COMMENT #6:\nLine 46: I suppose that the black circle character is a typo/not necessary.\nResponse: Thanks for the comment, we checked the submitted .pdf file and the .pdf file downloaded\nfrom the submission system but did not find the black circle characters. We assume this is raised by\na different kind or version of the .pdf reader. Apologies for this unclarity.\nSPECIFIC COMMENT #7:\nLine 46: you mentioned that you use air temperature (Temp). However, you do not explain what air\ntemperature is; the minimum, mean, maximum or other air temperature? Air temperature at what\nlevel?\nResponse: Thanks for the reviewer’s comment, we used maximum air temperature in the revised\nanalysis. We listed all the precursors in a Table in the Supplementary Material as we responded in\nGENERAL COMMENT #2.\nSPECIFIC COMMENT #8:\nLine 48: can you explain why did you classify Precipitation, which can be defined as the fall of\nwater, in any form (rain, snow, hail, hail, etc.), from the atmosphere to the earth's surface, as a\nhydrologic and not an atmospheric precursor?\nResponse: Thanks for the reviewer’s comment. In the new grouping system, we classify Precipitation\nas a top-down precursor. However, the precursor pre-selection (variable clustering) showed its\nlimited representativeness (Fig. S1), and as such precipitation was not included as one of the main\nprecursors.\nSPECIFIC COMMENT #9:\nLines 48-49: Please explain why you classify the fire WEATHER index (FWI) as a hydrologic and\nnot an atmospheric precursor. Please note that the Canadian Forest Fire Weather Index (FWI)\nSystem consists of six components that account for the effects of fuel moisture and WEATHER\nconditions on fire behavior, computed with noon air temperature, air relative humidity and wind\nspeed, and 24-hour accumulated precipitation.\nPlease quantify and explain how these misclassifications impact your findings.\nResponse: Thanks for the reviewer’s comment. We abandoned FWI as it will raise a conceptual\nproblem and we explained the reason above in SPECIFIC COMMENT #5:\n“Its inputs (noon air temperature, air relative humidity, 24-hour accumulated precipitation, and\nwind speed) have become part of the Top-down group of precursors in the revision, while other\ndirect measurements have been used to characterize bottom-up fuel status. To avoid this kind of\nconceptual problem, we have no longer included the FWI as a precursor in the revised analysis.”\nWe also discussed the limitations of this study including the impact of the grouping system as we\nresponded in GENERAL COMMENT #5:\nSecond, a better wildfire precursor grouping system may be beneficial. While we divided the\nprecursors into top-down and bottom-up groups, which are conceptually clear72, sometimes, the\ntop-down precursors dominate wildfires through their influence on fuel accumulation that is\ndeduced from their long time lags, making the causal inference complex. (Line 260-264)\nSPECIFIC COMMENT #10:\nLine 57 (Fig. 1a): Please explain why the sum of the partial correlations of all 12 fire precursors per\necoregion ranges between 0 and 2.\nResponse: Thanks for the reviewer’s question. The partial correlations are actually partial\ncorrelation coefficients (R), we summed the absolute value of partial correlation coefficients to find\nthe hotspot regions in the submitted version. Now we realize that the partial coefficient of\ndetermination (R2) is easier to understand, so we summed all the partial R2, in this case, the sum\nvalues should range between 0 and 1.\nFig 1. The sum of the partial coefficient of determination (partial R2) of all 10 wildfire precursors\nper ecoregion, with higher values meaning higher explained variance of burned area, and vice versa.\nSPECIFIC COMMENT #11:\nLines 57-61: “For all the 232 ecoregions, the dominant area fraction of the precursor (Fig. S1) shows\nthat VPD (47.6%) is the most important atmospheric precursor followed by Rad, Temp, and ET0\n(23.7%, 18.1%, 10.6%, respectively)”. What is the meaning of these results? Does this mean that\nVPD is the most important precursor in 47.6% of the 232 ecoregions, analyzed independently or\njointly? If I understand correctly, the sum of the percentages of the four precursors of each type is\n100%. Does this mean that you apply PCMCI independently for the precursors of each type/group\n(for all the ecoregions)? Please clarify.\nResponse: We appreciate the careful review. We only applied PCMCI once in each ecoregion and\nwe may get a dominant precursor belonging to either the top-down or bottom-up group. Then we\ndo the comparison in each group based on the dominant pixels. We changed the description as\nfollows:\nTo investigate the dominant wildfire precursors globally, we applied PCMCI to build causal\nnetworks for each of the 232 ecoregions (29 climate zones multiplied by 8 vegetation types),\nusing the top-down weather and bottom-up fuel grouping system. Then, in each ecoregion, we may detect\na dominant precursor belonging to either the top-down or bottom-up group according to whether\nthere is a causal relationship and the strength of the causal relationship (in this case partial\ncorrelation, Fig. 1). (Line 52-56)\nWe ranked the importance (dominant area fraction/pixel number) of the precursors in each\ngroup (Fig. S1). In the top-down group, ET0 is the most important precursor followed by VPD, AAI,\nTmax, and Wind, accounting for 41.1%, 32.0%, 16.6%, 10.3%, and 0% of top-down dominant\nregions, respectively. In the bottom-up group, SWDI is the most important precursor followed by\nGPP, EVI, FPAR, and NDVI, accounting for 56.4%, 21.4%, 11.1%, 8.6%, and 2.5% of bottom-up\ndominant regions, respectively. (Line 64-69)\nSPECIFIC COMMENT #12:\nLines 72-73: How did you compute these percentages? Did you use the number of ecoregions, the\narea, or what?\nResponse: We appreciate the reviewer’s careful review. We used the dominant pixel numbers to\ncompute these percentages and changed the description as:\nGlobally, the top-down group shows more dominance than the bottom-up group, accounting\nfor 73.3% and 26.7 % of the globe where causal relationships are detected (Fig. 1c). (Line 59-61)\nSPECIFIC COMMENT #13:\nLine 83 and 91: this is only due to the precursors you selected and how you classify them.\nResponse: Thanks for the reviewer’s comment. We believe that our revised top-down and bottom-\nup grouping system addresses this concern, and we revised the relevant statements as follows:\nWildfires are positively related to top-down precursors globally (91.6% positive, Fig. 1d). This\npositive relationship indicates that in flammability-limited regions, BA will increase when more\nenergy is supplied (higher Tmax and ET0) or the environment is drier (higher VPD and AAI). This\nis supported by evidence indicating that wildfires increase with the increased frequency and\nintensity of summer heatwaves25, the strengthened solar radiation absorption26, 27, and the increased\natmospheric water demand and drought conditions28, 29 (see Fig. S2 for ET0, AAI, and VPD\ndominance). Major climate modes (e.g., El Niño–Southern Oscillation, and North Atlantic\nOscillation) also modulate the occurrence of these weather anomalies30, 31, 32, 33, 34 and as such play\nan important role in governing global patterns of wildfire activities33, 35, 36. The top-down dominance\nin the rainforest is supported by studies showing that here wildfires are limited to the time window\nwhen fuels are dry enough to burn37, 38, see Fig. S2 for the AAI dominance. (Line 86-97)\nSPECIFIC COMMENT #14:\nLine 87: another black circle!\nResponse: Thanks for the comment, and apologies for this inconvenience which may be related to\ndifferent pdf readers.\nSPECIFIC COMMENT #15:\nLines 87-89: An obvious and unnecessary sentence. Is there any region where the atmospheric\nprecursors do not play an important role?\nResponse: Thanks for the reviewer’s detailed suggestion to improve the statement of this manuscript.\nWe revised the relevant statement as follows:\nThe top-down dominance in the rainforest is supported by studies showing that here wildfires\nare limited to the time window when fuels are dry enough to burn37, 38, see Fig. S2 for the AAI\ndominance. (Line 95-97)\nSPECIFIC COMMENT #16:\nLines 91-92: “Fires tend to be negatively related to hydrologic precursors (72.6%), and less\nfrequently this relationship is positive (27.4%), see Fig. 1d”; the part “, and less frequently this\nrelationship is positive (27.4%),” of the sentence is completely unnecessary; the reader can easily\ncompute 100-72.6=27.4.\nResponse: Thanks for the reviewer pointing this out. We revised the relevant statement as follows:\nWildfires are positively related to top-down precursors globally (91.6% positive, Fig. 1d). (Line 86-\n87)\nWildfires tend to be negatively related to bottom-up precursors (78.9% negative, Fig. 1d). (Line 98)\nSPECIFIC COMMENT #17:\nLines 92-94: another obvious and unnecessary sentence; it is obvious that drought and precipitation\ndeficits may provide optimal conditions for wildfires while the opposite, a “high water supply” can\nprevent fires.\nResponse: Thanks for the reviewer’s comment. We deleted redundant and unnecessary sentences\nthroughout the manuscript.\nSPECIFIC COMMENT #18:\nLines 94-96: What “dominant positive relationship”?\nResponse: Thanks for the reviewer’s comment. As we changed the grouping system, there is no more\nhydrologic group and this sentence has been removed from the revised manuscript. We checked the\nstatements about partial correlation relationships (sign) and avoided such ambiguous statements.\nSPECIFIC COMMENT #19:\nLine 110: I am confused. If “All the data sets are averaged or summed up to weekly” (line 287),\nhow can you have assessed the partial correlation for time lags shorter than 1 week?\nResponse: Thanks for the reviewer pointing this out and we regret such an inaccurate statement.\nWe revised it as:\nLonger time lags are shown in the boreal forests, African and Australian savannahs, the Indian\nPeninsula, the Indochina Peninsula, and southeastern South America (Fig. 2a). In the other regions,\nwildfires are a direct result of the precursing conditions (mostly no more than 1 week). (Line 111-\n113)\nSPECIFIC COMMENT #20:\nLines 112-113: “…, which means in such regions fires are ignited shortly after (within a few days)\nthe changes in the fire precursors.” another obvious and unnecessary sentence.\nResponse: Thanks for the reviewer’s comment. We deleted such redundant sentences in the\nmanuscript and made the statements more concise and meaningful as follows:\nIn the other regions, wildfires are a direct result of the precursing conditions (mostly no more than\n1 week). (Line 112-113)\nThe bottom-up precursors can also come with negligible time lags (0 weeks, Fig. 2b), mainly in\nAustralia and South America (Fig. 2c and Fig. S7), indicating that they can serve as not only fuel\naccumulation (longer time lag) but also fuel dryness (short time lag) indicators. (Line 127-130)\nSPECIFIC COMMENT #21:\nLines 118-119: what evidence do you have for such a statement? That should not be the reason, at\nleast everywhere.\nResponse: Thanks for the question. We cited a paper to support our explanation. A time lag of half\na year is likely to follow from a peak in vegetation productivity and thus fuel accumulation in the\nwet season, which is then followed by fires in the subsequent dry season.\nIn this case, one possible reason is that the top-down precursors affect wildfires through their\ninfluence on fuel accumulation and availability. This is supported by a study conducted by Murray-\nTortarolo et al., 201641 indicating that in dry ecosystems, the dry season precipitation affects annual\nnet primary productivity. (Line 118-121)\nSPECIFIC COMMENT #22:\nLines 125-127: this does not seem correct/accurate; the tropics are a very large region; it is possible\nto be much more accurate. The explanation does not also seem correct. Please note that 30 weeks is\nless than a year.\nResponse: Thanks for pointing this out. We detailed the regions and revised the sentence as follows:\nThe bottom-up precursors show long time lags, especially in Europe, Asia, North America, and\nAfrica (more than half a year, Fig. 2c). One possible reason could be that the current vegetation\nstatus or accumulated fuel is related to water supply and vegetation growth in the pre-wildfire\nseasons40, 42. This is supported by studies showing that spring greening related to global warming\ncan either be positively or negatively related to total biomass43, 44, through the carryover effect of\ncurrent vegetation states on subsequent growth caused by increased photosynthesis or earlier soil\nmoisture depletion39, 40. (Line 122-127)\nWe cited the “compensation effects of spring warming” related studies, especially reference 44, to\nsupport our analysis. In their study, they found that annual spring temperature affects subsequent\nsummer and autumn NDVI. If we take the pre-growing season into account, this carryover effect\nwould last longer from pre-growing to the whole growing season. This is also supported by Murray-\nTortarolo et al. (2016).\nReference：\nBuermann W, Forkel M, O'Sullivan M, Sitch S, Friedlingstein P, Haverd V, et al. Widespread\nseasonal compensation effects of spring warming on northern plant productivity. Nature 2018,\n562(7725): 110-114.\nMurray‐Tortarolo G, Friedlingstein P, Sitch S, Seneviratne SI, Fletcher I, Mueller B, et al. The dry\nseason intensity as a key driver of NPP trends. Geophysical Research Letters 2016, 43(6): 2632-\n2639.\nSPECIFIC COMMENT #23:\nLine 140 and 154: area of what?\nResponse: Thanks for the reviewer’s careful review. We revised them as follows:\nTo generalize the previous analysis, we evaluated the dominance changes (according to the\ndominant pixel number) of top-down and bottom-up precursors that are primarily driven by energy,\ndryness, vegetation, and location (Tmax, VPD, EVI, and latitude, Fig. 3). (Line 143-145)\nWe replaced the “dominant area fraction” with “dominant fraction” and explained how the\ndominant fraction is calculated.\nThe dominant fraction that is primarily driven by the group (top-down or bottom-up), is binned\nby temperature, AAI, NDVI, and latitude. The fractions were calculated by dividing the number of\npixels where a specific precursor group is dominant by the number of pixels located in a specific\nbin and dominated by any of the two groups. (Line 159-162)\nSPECIFIC COMMENT #24:\nLine 154: another black circle.\nResponse: Thanks for the comment, and apologies for this inconvenience which may be related to\ndifferent pdf readers.\nSPECIFIC COMMENT #25:\nLine 165: another black circle.\nResponse: Apologies for this inconvenience.\nSPECIFIC COMMENT #26:\nLine 196: in the Köppen-Geiger climate classification, the B type of climate is arid, not dry. Please\ncorrect.\nResponse: Thanks for pointing this out, we have corrected this in the revision.\nSPECIFIC COMMENT #27:\nLines 254-270 (data): Apparently, you used monthly fire data can the reader assume that the time\nseries of all other variables are also monthly? If not (line 287), how did you proceed to analyze data\nwith different temporal sampling? Datasets do not have the same spatial resolution; how did you\nprocess the data with different spatial resolutions?\nResponse: Thanks for the question. We listed the original spatial and temporal scale of the data sets\nin the Supplementary Material, as explained in GENERAL COMMENT #2. We also modified the\nData section for further clarification:\nAll the above-mentioned data sets span from 2003 to 2020. Since not the entire world surface\nhas experienced wildfires in the past decades, we performed the analysis based on ecoregions\ninstead of at a pixel level. In each of the 232 ecoregions (29 climate zones and 8 land cover classes),\nBA and precursors were resampled to a spatial resolution of 0.25° and averaged (precursors) or\nsummed up (BA) to weekly values. Then, we calculated the ecoregional mean (precursors) or sum\n(BA) values by averaging or summing up all the pixels located in the ecoregions. (Line 301-306)\nSPECIFIC COMMENT #28:\nLines 262-263: Why did you use temperature, radiation, and precipitation of the Global Land Data\nAssimilation System (GLDAS) but ET0, VPD, FWI and AAI from ERA5? Why did you not use all\nweather data from the same source, for example, ERA5, which provides you with not only global\nbut also consistent (homogeneous) data?\nResponse: We appreciate the reviewer’s constructive data suggestions. In this revised manuscript,\nwe followed your suggestion by consistently using data from ERA5 and MODIS.\nSPECIFIC COMMENT #29:\nLines 271-285: the reader may not have access to all publications cited in the manuscript; this is\nwhy the methodology must be, eventually briefly but clearly described, which is not the case. In\naddition, the description of how AAI was computed is unclear; for example, “For each year, we\ncalculated the difference between AI at a certain period and the normal AI for the corresponding\nperiod to calculate the anomaly, then the AAI was obtained.”\nResponse: Thanks. We revised the Methods to make everything clear and listed the equations of how\nAAI, VPD, and SWDI are calculated in the Supplementary Material as follows:\nWe used a linear condition independence test, partial correlation, in PCMCI (both the\ncondition selection and condition independence test stages) and set a significance level of 0.05 and\na maximum time lag of 156 weeks. In each of the 232 ecoregions, we input wildfire behavior\nindicator (BA), Top-down precursors (Tmax, VPD, ET0, Wind, and AAI), and Bottom-up precursors\n(FPAR, GPP, NDVI, EVI, and SWDI) and applied PCMCI to get causal networks showing causal\nlinks, directions, strengths, and time lags. In this study, we only focus on causal links and the time\nlags from wildfire precursors to BA. In each ecoregion, after getting the causal network, only the\nprecursors that show causal links to BA are ranked by causal strength (partial correlation), and the\nprecursor ranked first is called the dominant precursor, and its time lag is called the dominant time\nlag. (Line 332-343)\nPrecursor Calculation\nAridity Anomaly Index (AAI)\nAAI was developed by the Indian Meteorological Department. It is a real-time drought index that describes vegetation water\nstress. In this study, AAI was calculated by dividing the difference between potential evaporation and actual evaporation by\npotential evaporation, the formula is shown below. The potential evaporation and actual evaporation are both from ERA5.\n(cid:1831)(cid:1846) −(cid:1831)(cid:1846)\n(cid:1827)(cid:1827)(cid:1835)=100× (cid:3028) (cid:2868)\n(cid:1831)(cid:1846)\n(cid:2868)\nwhere (cid:1831)(cid:1846) is evaporation and (cid:1831)(cid:1846) is potential evaporation.\n(cid:3028) (cid:2868)\nVapor Pressure Deficit (VPD)\nVPD indicates the ability of the atmosphere to extract water from the environment by measuring the difference between the\namount of water vapor in the air and how much water vapor it can hold when the air is saturated. In this study, VPD was calculated\nusing minimum and maximum 2m air temperature and dewpoint temperature from ERA5. The formulas are shown below. All the\ntemperature products used are from ERA5.\n(cid:1848)(cid:1842)(cid:1830)=(cid:1857) −(cid:1857)\n(cid:3046) (cid:3028)\n((cid:1857) +(cid:1857) )\n(cid:1857) = (cid:3047)(cid:3040)(cid:3028)(cid:3051) (cid:3047)(cid:3040)(cid:3036)(cid:3041)\n(cid:3046) 2\n17.27×(cid:1846)\n(cid:1857) =0.6108×(cid:1857)(cid:1876)(cid:1868)(cid:3436) (cid:3031)(cid:3032)(cid:3050)(cid:3440)\n(cid:3028) (cid:1846) +237.3\n(cid:3031)(cid:3032)(cid:3050)\n17.27×(cid:1846)\n(cid:1857) =0.6108×(cid:1857)(cid:1876)(cid:1868)(cid:3436) (cid:3040)(cid:3028)(cid:3051)(cid:3440)\n(cid:3047)(cid:3040)(cid:3028)(cid:3051) (cid:1846) +237.3\n(cid:3040)(cid:3028)(cid:3051)\n17.27×(cid:1846)\n(cid:1857) =0.6108×(cid:1857)(cid:1876)(cid:1868)(cid:3436) (cid:3040)(cid:3036)(cid:3041)(cid:3440)\n(cid:3047)(cid:3040)(cid:3036)(cid:3041) (cid:1846) +237.3\n(cid:3040)(cid:3036)(cid:3041)\nwhere (cid:1857) is saturation vapor pressure and (cid:1857) is actual vapor pressure. (cid:1846) is dewpoint temperature, (cid:1846) is maximum air\n(cid:3046) (cid:3028) (cid:3031)(cid:3032)(cid:3050) (cid:3040)(cid:3028)(cid:3051)\ntemperature, and (cid:1846) is minimum air temperature.\n(cid:3040)(cid:3036)(cid:3041)\nSoil Water Deficit Index (SWDI)\nSWDI is an agricultural drought monitoring index that uses soil moisture (SM), field capacity (FC), and wilting point (WP).\nThe formula can be found below. Soil moisture used in this study is from ERA5 (volumetric soil water layer 1). Field capacity and\nwilting point are from Montzka et al., 20171. (Supplementary Material Line 7-30)\nAnomaly Calculation\n(cid:1877)−(cid:1865)(cid:1857)(cid:1853)(cid:1866)\n(cid:1827)(cid:1866)(cid:1867)(cid:1865)(cid:1853)(cid:1864)(cid:1877)=\n(cid:1871)(cid:1872)(cid:1856)\nwhere (cid:1877) is the value of a time series at a given time point; (cid:1865)(cid:1857)(cid:1853)(cid:1866) is the mean of all values at the same time point in all cycles, in\nour case, it is the mean of all values for the same week in 18 years (2003–2022); (cid:1871)(cid:1872)(cid:1856) is the standard deviation derived from all\nvalues for the same week of the year across the 18 years. (Supplementary Material Line 55-59)\nSPECIFIC COMMENT #30:\nLines 286-305 (Data Processing Procedures): you mentioned that you use monthly fire data (line\n256 “…It is a global and monthly BA product”) and “All the data sets are averaged or summed up\nto weekly values…”. Please explain this apparent contradiction.\nSo, you produce weekly time series for each of the 28 climate zones (from Köppen-Geiger) and 8\nland cover classes (from ESA CCI). The questions are:\nWhat was the original temporal resolution of the downloaded data?\nHow did you proceed to obtain the time series for each of the 232 ecoregions 28 (or 29) climate\nzones and 8 vegetation types?\nResponse: Thanks for the comment. We revised the Data section and added several sentences\nregarding FireCCI as follows:\nNote that even though FireCCI51 is a monthly dataset, it includes a layer recording the first\nfire detection day. Therefore, we computed the weekly summed BA from this daily information. For\nthe precursors, we calculated the weekly mean from their original temporal scale. (Line 306-309)\nWe made a Table in the Supplementary Material to show the original temporal and spatial resolution\nof the downloaded data as listed in GENERAL COMMENT #2.\nWe also clarified how we obtained the time series in each of the 232 ecoregions as follows:\nIn each of the 232 ecoregions (29 climate zones and 8 land cover classes), BA and precursors\nwere resampled to a spatial resolution of 0.25° and averaged (precursors) or summed up (BA) to\nweekly values. Then, we calculated the ecoregional mean (precursors) or sum (BA) values by\naveraging or summing up all the pixels located in the ecoregions. (Line 303-306)\nSPECIFIC COMMENT #31:\nLine 294: Please clarify what is a “… PC condition selection …”; all acronyms must be clearly\ndefined.\nResponse: Thanks for the comment. We revised the statement as follows.\nIt consists of two stages: a Peter & Clark (PC) condition selection stage and a Momentary\nConditional Independence (MCI) test stage. (Line 317-318)\nSPECIFIC COMMENT #32:\nLines 294-295: Please clarify what is “…an MCI conditional independence test …”; all acronyms\nmust be clearly defined.\nResponse: Thanks for the comment. Please see SPECIFIC COMMENT #31.\nSPECIFIC COMMENT #33:\nLine 295: “In the first stage, irrelevant conditions are removed”; what is an irrelevant condition?\nThanks for the reviewer’s careful comment. We revised the relevant statement as follows:\nIn the first stage, for each variable, the irrelevant conditions that do not pass the iterative\nindependence test are removed based on a Markov set discovery algorithm. In the next stage, a\nfalse-positive control was adapted to highly relevant interdependent conditions from the first stage.\n(Line 318-321)\nSPECIFIC COMMENT #34:\nLines 296-298: “Two main assumptions of PCMCI are time-series stationarity and causal\nsufficiency. The first assumption can be satisfied by calculating anomalies and de-trending the time\nseries.” Once again, the authors do not adequately explain what leads to doubt. First, obviously, it\nis not by calculating anomalies that the series becomes stationary; just think that the trend does not\ndisappear. Second, you don't explain what trend you remove and how. Did you just remove the trend\nover the entire study period? Did you check and correct the seasonality? This whole procedure must\nbe properly explained.\nResponse: We appreciate the reviewer’s comment. We first removed the entire period trend by fitting\na linear regression and then calculated the anomaly to remove the seasonal cycle. By doing this, we\nensured stationarity. We revised the relevant statements as follows:\nWe aimed to achieve (a) causal sufficiency by involving a wild range of wildfire precursors,\nand (b) time series stationarity by detrending and calculating anomalies. We used linear regression\nto remove the trend over the entire period and calculated anomalies to remove the seasonal cycle\nand alleviate temporal autocorrelation. The formula for the anomaly calculation can be found in\nthe Supplementary Material. (Line 330-334)\nREVIEWER COMMENTS\nReviewer #1 (Remarks to the Author):\nGeneral comments\nThe authors addressed my comments, and the manuscript has improved. However, several points remain\nunclear. I have further comments as below:\nMajor comments\n• The authors confirmed that the analysis is based on ecoregional mean/summafion. However, this\napproach does not fully exploit the available high-resolufion data (i.e., 0.25x0.25 degree). It potenfially\nleads to losing important informafion because the results have lower spafial resolufion compared to the\ninput data. It is probably fine to use the regional mean of the precursors as it is assumed that in the\nselected region, the climate condifions are similar for all grid points. However, using the summed BA\nmight not be appropriate to represent the diverse fire condifions for all grid points/pixels. In this case, I\nthink a more appropriate approach can be as follows: applying the causal analysis using the regional\nmean of precursors and BA data of all grid points. Other tradifional approach is to have the\ncomputafions for all grid points separately (i.e., at a selected grid point only climate and BA data of that\ngrid points is used).\n• Please clarify if the current approach using ecoregional mean/summafion is the only available one?\n• If there are other methods available (e.g., my suggesfions above), which method is befter (i.e.,\nproviding more detailed and accurate results)?\n• The authors argued that they must use the regional mean/summafion because not all grid points\nhaving BA data. This is not convincing because for these grid points with no fire, the obtained results\nare automafically no causal impacts (without computafions).\nMinor comments\n• The range of the areas of the ecoregions needs to be provided.\nReviewer #2 (Remarks to the Author):\nDear authors,\nI believe that you have safisfactorily answered the quesfions raised and proceeded with the adequate\nrevision of the manuscript according to the suggesfions provided in the revision report.\nI recommend that the manuscript can be accepted for publicafion.\nReviewer #1 (Remarks to the Author):\nGeneral comments\nThe authors addressed my comments, and the manuscript has improved. However, several points\nremain unclear. I have further comments as below:\nResponse: We appreciate the positive comment of the reviewer on our revised manuscript. Our\nresponses to the reviewer’s comments are given below.\nMajor comments\n• The authors confirmed that the analysis is based on ecoregional mean/summation. However, this\napproach does not fully exploit the available high-resolution data (i.e., 0.25x0.25 degree). It\npotentially leads to losing important information because the results have lower spatial resolution\ncompared to the input data. It is probably fine to use the regional mean of the precursors as it is\nassumed that in the selected region, the climate conditions are similar for all grid points. However,\nusing the summed BA might not be appropriate to represent the diverse fire conditions for all grid\npoints/pixels. In this case, I think a more appropriate approach can be as follows: applying the\ncausal analysis using the regional mean of precursors and BA data of all grid points. Other\ntraditional approach is to have the computations for all grid points separately (i.e., at a selected grid point only climate and BA data of that grid point are used).\n• Please clarify if the current approach using ecoregional mean/summation is the only available one?\n• If there are other methods available (e.g., my suggestions above), which method is better (i.e.,\nproviding more detailed and accurate results)?\n• The authors argued that they must use the regional mean/summation because not all grid points\nhave BA data. This is not convincing because for these grid points with no fire, the obtained results\nare automatically no causal impacts (without computations).\nResponse: We appreciate the constructive comments and suggestions provided by the reviewer to\nimprove our manuscript. We agree that there are other approaches for spatiotemporal data\naggregation to perform this causal study, e.g., at a pixel scale by using original pixel BA and wildfire\nprecursor values or using original pixel BA value and ecoregional mean wildfire precursor values.\nIn this study, we performed the causal analysis at an ecoregional scale because: (i) We expect both\nbottom-up fuel and top-down weather conditions to be relatively similar within an ecoregion as the\nareas within ecoregions are by definition comparable in climate, soil, geology, topography, and\nvegetation1. (ii) We want to generate a wildfire information system like a “look-up table” based on\nthe climate zone and vegetation type of a region. Such a “look-up table” provides basic insights\nneeded to develop a wildfire warning/predicting system for regions (even if the region may be\nsmaller than a 0.25°x 0.25° pixel) where we don’t have enough in-situ or field data. In this respect,\nour ecoregional scale study is more appropriate to generalize findings at a global scale than pixel\nscale study, yet still allows for sufficient spatial variation when analyzing global fire regimes.\nThere are several notable wildfire studies also at the ecoregional scale. For example, Jones et al.,\n20222 reviewed the grid and ecoregional scale trends and drivers of wildfires to relate their study\nto previously published grid-scale and (eco)regional-scale studies. In their study, they used three\n(eco)regional layers including 14 macroregions from the Global Fire Emissions Database (GFED),\nten ecoregions, and countries. Turco et al., 20183 studied wildfires in Mediterranean Europe in 44\necoregions defined by both wildfire information and environmental zones. Walker et al., 20204\nstudied the dominant drivers of boreal wildfire severity according to four ecoregions grouped from\n417 field sites. Williams et al., 20195 studied the impacts of anthropogenic climate change on\nwildfires in California based on four ecoregions defined by vegetation types, climate conditions,\nwildfire information, and population density. Alizadeh et al., 20236 studied the trends of wildfire\ndanger in mountain regions of the western US by averaging pixel scale data to 15 ecoregions. Littell\net al., 20187 assessed future wildfires in the western US in ecoregions defined by wildfire climate\ncontrols.\nThese studies further demonstrate that the ecoregional scale is an appropriate level for assessing\nbottom-up fuel and top-down weather influences on wildfire activity. To avoid confusion, we have\nrephrased our statement of the revision:\n“We performed the causal analysis at the ecoregional level considering that top-down weather and\nbottom-up fuel conditions are relatively similar within the same ecoregion76. We believe that this\nspatial aggregation is appropriate to generalize findings at a global scale, yet still allows for\nsufficient spatial variation when analyzing global fire regimes. In addition, we can generate a “look-\nup table” based on ecoregions that provide basic insights needed to develop a wildfire\nwarning/predicting system for locations where we do not have enough in-situ or field data.” (Line\n301-307)\nReference:\n1. Hughes RM, Omernik JM. Ecological regions (ecoregions). In: Environmental Geology).\nSpringer Netherlands (1999).\n2. Jones MW, et al. Global and regional trends and drivers of fire under climate change. Reviews\nof Geophysics, (2022).\n3. Turco M, et al. Exacerbated fires in Mediterranean Europe due to anthropogenic warming\nprojected with non-stationary climate-fire models. Nat Commun 9, 3821 (2018).\n4. Walker XJ, et al. Fuel availability not fire weather controls boreal wildfire severity and carbon\nemissions. Nature Climate Change 10, 1130-1136 (2020).\n5. Williams AP, et al. Observed Impacts of Anthropogenic Climate Change on Wildfire in\nCalifornia. Earth's Future 7, 892-910 (2019).\n6. Alizadeh MR, et al. Elevation-dependent intensification of fire danger in the western United\nStates. Nat Commun 14, 1773 (2023).\n7. Littell JS, McKenzie D, Wan HY, Cushman SA. Climate Change and Future Wildfire in the\nWestern United States: An Ecological Approach to Nonstationarity. Earth's Future 6, 1097-\n1111 (2018).\nMinor comments\n• The range of the areas of the ecoregions needs to be provided.\nResponse: Thanks for the reviewer’s comment. We added two figures in the Supplementary Material\nto show the range of the ecoregions according to climate zones and vegetation types:\nFig S16. Köppen-Geiger climate zone. The main climates include A (equatorial), B (arid), C (warm\ntemperate), D (Snow), and E (polar). The precipitation types include W (desert), S (steppe), f (fully\nhumid), s (summer dry), w (winter dry), and m (monsoonal). The temperature types include h (hot\narid), k (cold arid), a (hot summer), b (warm summer), c (cool summer), d (extremely continental),\nF (polar frost), and T (polar tundra).\nFig S17. Vegetation type from the European Space Agency (ESA) Climate Change Initiative (CCI).\nThe vegetation types include sparse vegetation, grassland, cropland, shrubland, tree cover broad-\nleaf evergreen (TBE), tree cover broad-leaf deciduous (TBD), tree cover needle-leaf evergreen\n(TNE), tree cover needle-leaf deciduous (TND).\nReviewer #2 (Remarks to the Author):\nDear authors,\nI believe that you have satisfactorily answered the questions raised and proceeded with the adequate\nrevision of the manuscript according to the suggestions provided in the revision report.\nI recommend that the manuscript can be accepted for publication.\nResponse: We appreciate the reviewer’s contributions to improve this manuscript.\nREVIEWERS' COMMENTS\nReviewer #1 (Remarks to the Author):\nThe authors addressed my comments. I may recommend publicafion of this work."}]

Decision letter