# Strong and Rapid Postfire Recovery of Vegetation Productivity in Australia

Li Pan, Xiangming Xiao, Yuanwei Qin, Josep G Canadell, Alfredo Huete, Philippe Ciais, Shenglai Yin, Chenchen Zhang, Baihong Pan, Chenglong Yin, et al.

► To cite this version:

Li Pan, Xiangming Xiao, Yuanwei Qin, Josep G Canadell, Alfredo Huete, et al. Strong and Rapid Postfire Recovery of Vegetation Productivity in Australia. Global Change Biology, 2025, 31 (7), 10.1111/gcb.70321. hal-05173251

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RESEARCH ARTICLE

# Strong and Rapid Postfire Recovery of Vegetation Productivity in Australia

Li Pan<fnref n="1" /> | Xiangming Xiao<fnref n="1" /> ID | Yuanwei Qin² | Josep G. Canadell³ | Alfredo Huete⁴ ID | Philippe Ciais⁵ | Shenglai Yin<fnref n="1" /> | Chenchen Zhang<fnref n="1" /> | Baihong Pan<fnref n="1" /> | Chenglong Yin⁶ | Cheng Meng<fnref n="1" /> | Yuan Yao<fnref n="1" /> | Haoming Xia⁷

**Correspondence:** Xiangming Xiao (xiangming.xiao@ou.edu)

**Received:** 15 February 2025 | **Revised:** 11 June 2025 | **Accepted:** 12 June 2025

**Funding:** This work was supported by National Science Foundation (OIA-1920946 and OIA-1946093).

**Keywords:** biological adaptations and evolutionary processes | ecosystem resilience | gross primary productivity (GPP) | post-fire carbon dynamics | post-fire vegetation recovery | precipitation effects on recovery | wildfire

## ABSTRACT

Fires disrupt ecosystems, release carbon, and reduce carbon uptake, which increases atmospheric CO₂ concentration, warms the atmosphere, and fosters more frequent and intense fires. Quantifying postfire recovery is crucial for understanding the adaptability and resilience of ecosystems to fire disturbances. Observations from satellite-derived active fire (~1-km) and Gross Primary Productivity (GPP) products reveal that Australia experiences extensive fires annually, reducing vegetation productivity. Here we analyze the post-fire GPP recovery trajectories of 1.7×10⁶ fire-affected pixels (or 1.5×10⁶ km²) in Australia between 2011 and 2019, of which 1.3×10⁶ pixels (1.2×10⁶ km²) experienced a single fire (single-fire pixels), and 0.4×10⁶ pixels (0.3×10⁶ km²) experienced two or more fires (multiple-fire pixels). We found that Australia's postfire GPP recovery was strong and rapid. 88% of single-fire pixels recovered to 135% of the prefire level in an average of 2.3 years, whereas 86% of multiple-fire pixels recovered to 115% of the prefire level in an average of 1.2 years. NonForest ecosystems (e.g., grasslands, shrublands, and savannas) exhibited a higher postfire recovery magnitude (138% for single-fire pixels and 115% for multiple-fire pixels) compared to Forest (110% for single-fire pixels and 108% for multiple-fire pixels). This rapid and robust postfire GPP recovery is significantly influenced by postfire precipitation, fire (i.e., fire frequency, intensity) and fire severity (damage, impacts; a metric of resistance of terrestrial ecosystems to fire). Specifically, higher fire severity and higher postfire precipitation have a positive impact on postfire recovery, whereas increased fire frequency has a negative impact. Furthermore, fire dynamics have a smaller role in the long-term interannual continental GPP changes than climate or land-use changes, as strong and rapid GPP recovery offsets the short-term fire-induced GPP losses.

## 1 | Introduction

Wildfires are becoming increasingly frequent globally, driven by climate variability (and trends) (Canadell et al. 2021; Jones et al. 2022). Fires directly alter terrestrial ecosystems by

disrupting their structure and function, altering soil composition, reducing biodiversity, and impairing vegetation recovery, which can lead to ecosystem degradation (Canadell and Jackson 2021). Additionally, fire emissions reduce terrestrial carbon stocks and increase atmospheric carbon dioxide concentrations, triggering

<footnote n="1">¹School of Biological Sciences, Center for Earth Observation and Modeling, University of Oklahoma, Norman, Oklahoma, USA | ²Jiangsu Key Laboratory of Soil and Water Processes in Watershed, College of Geography and Remote Sensing, Hohai University, Nanjing, China | ³CSIRO Environment, Canberra, Australian Capital Territory, Australia | ⁴School of Life Sciences, University of Technology Sydney, Sydney, New South Wales, Australia | ⁵Laboratoire des Sciences du Climat et de l'Environnement, LSCE/IPSL, CEA-CNRS- UVSQ, Université Paris-Saclay, Gif-sur-Yvette, France | ⁶School of Geographic Sciences, East China Normal University, Shanghai, China | ⁷College of Geography and Environmental Science, Henan University, Kaifeng, China</footnote>
feedback effects on the climate system that further escalate fire risk, resulting in fire cycles (van der Velde et al. 2021; Zhao et al. 2024; Zheng et al. 2023).

Australia experiences frequent and intense fires that pro-
foundly shape its ecosystems and carbon cycling (van der Velde
et al. 2021; Villalobos et al. 2023). Large-scale fire events di-
rectly affect the release of terrestrial carbon in Australia (Burton
et al. 2024). One study estimates that fires in Australian grass-
lands emitted around 280 Tg CO₂ per year between 1997 and
2013 (Beringer et al. 2015). During the extreme 2019 south-
eastern Australia forest fire event, results from five inventories
indicate total carbon emissions of approximately 275 Tg CO₂;
however, these figures may still be conservative, as alternative
assessments based on atmospheric CO measurement suggest
that emissions could have reached 517–867 Tg CO₂ (van der
Velde et al. 2021), underscoring the vast scale of fire-induced
carbon release in Australia.

Beyond the direct effect of carbon emissions, biomass mortal-
ity and the loss of functional leaf area due to fire significantly
weaken the capacity of ecosystems to sequester carbon (Beringer
et al. 2007; Cernusak et al. 2006). In a northern Australian sa-
vanna, for instance, fire transformed the ecosystem from a car-
bon sink into a carbon source, primarily because of the marked
decline in postfire productivity—an impact on the carbon bal-
ance comparable to direct fire emissions (Beringer et al. 2007).
Similarly, in a semiarid woodland in southern Australia, GPP
decreased by 65% following a fire (Sun et al. 2016). These consid-
erable carbon emissions and the associated reduction in carbon
uptake can sharply diminish aboveground biomass, and in cer-
tain years, their influence on the carbon balance even surpasses
that of climatic factors. For example, one study indicates that
the 2019 southeastern Australia forest fire event was responsible
for 85% of the total annual biomass loss (Qin et al. 2022), with
the remainder attributed to drought and land clearing. Overall,
existing research suggests that fires are a primary driver of high
interannual variability in carbon fluxes across the Australian
continent.

Carbon losses from fires are offset by postfire vegetation recovery (Bartels et al. 2016; Qin et al. 2022). The postfire recovery trajectory reflects the natural fire-adaptation mechanisms that Australian ecosystems have developed over long-term evolutionary processes (Karavani et al. 2018; Nolan et al. 2021). These processes are influenced by various factors, including fire behavior (e.g., intensity and frequency), climatic conditions (e.g., precipitation), and biome characteristics (Nolan et al. 2021; Sun et al. 2016; Turner 2010), such that at large spatial scales, different regions and biomes with varying biomass densities may exhibit divergent postfire recovery trajectories. This spatial and biome-specific heterogeneity shapes postfire carbon dynamics, encompassing spatiotemporal patterns of carbon fluxes, carbon sinks, biomass, and atmospheric CO₂. To date, current studies on postfire vegetation recovery in Australia mainly focus on individual fire events or short-term impacts (Beringer et al. 2007; Byrne et al. 2021; Cernusak et al. 2006; Driscoll et al. 2024; Sun et al. 2016), leaving a gap in our comprehensive understanding of recovery trajectories over broader temporal and spatial scales. Systematically and comprehensively quantifying postfire recovery processes is therefore critical for deepening our

understanding of ecosystem resilience to fire disturbances and
the positive feedback mechanisms linking fires and climate
(Byrne et al. 2021; Williams et al. 2004). In addition, considerable
uncertainty remains regarding fire-induced carbon anomalies
and vegetation dynamics simulations in current regional-scale
ecosystem models (Hantson et al. 2016; Quegan et al. 2011; Van
Der Werf et al. 2017). If the terrestrial ecosystem response to
wildfires can be accurately captured through long-term obser-
vations, it will furnish ecosystem process models with more ro-
bust parameters and algorithms, thereby enhancing the capacity
to evaluate carbon budgets, fire risk, and climate change across
broader spatial and temporal scales.

Here, we analyzed the short-term postfire GPP recovery of all pixel-fire events (i.e., fires in forests, savannas, shrublands, and grasslands) across Australia from 2011 to 2019, using GPP data from 07/2000 to 06/2023. The analyses included all 1.7 × 10⁶ fire-affected pixels (1.5 × 10⁶ km²) across Australia during the study period, comprising 1.3 × 10⁶ single-fire pixels (1.2 × 10⁶ km²) and 0.4 × 10⁶ multiple-fire pixels (0.3 × 10⁶ km²), the two categories being distinguished because fire frequency influences postfire recovery. We investigated three scientific questions: (1) What are the postfire GPP recovery trajectories (i.e., recovered/uncovered, magnitude, slope, and time) in single-fire and multiple-fire pixels? (2) How do postfire precipitation and fire severity influence recovery? and (3) What are the relative roles of fire on the long-term interannual GPP change? The multi-year record of active fire and GPP products derived from satellites enables a comprehensive understanding of postfire recovery trajectories to an extent not previously possible (Bao et al. 2024; Qin et al. 2022; Woodgate et al. 2025; Xu et al. 2024). A deeper understanding of postfire GPP recovery trajectories and their long-term impacts is essential for assessing ecosystem resilience, anomalies in atmospheric CO₂, and climate change dynamics.

2 | Materials and Methods

2.1 | Forest and NonForest Map

Differences in biomass (herbaceous and woody) and ecosystem
structure (understory and overstory vegetation, canopy, cover,
and leaf area) among various land cover and plant functional
types may result in differing responses to fire and postfire re-
covery. Therefore, we separately analyzed postfire recovery in
Forest and NonForest (grassland, savanna, scrublands, etc.). We
used the forest map in 2010 developed by Qin et al. (2021), which
employed PALSAR imagery and Moderate Resolution Imaging
Spectroradiometer (MODIS) imagery to generate a 50-m spatial
resolution forest map of Australia (Figure S1). This map used the
forest definition (areas exceeding 0.5 ha with a tree canopy cover
greater than 10% and trees reaching a height of over 5 m) pro-
vided by the United Nations Food and Agriculture Organization
(FAO). It is important to note that the Australian government
has its unique definition of forest (areas exceeding 0.2 ha with a
tree canopy cover greater than 20% and trees reaching a height
of over 2 m), which includes shrubs and short trees. In Text S1,
we compared the forest maps using FAO's forest definition with
those using the Australian government forest definition by
National Forest Inventory Steering (2019). Different forest maps
lead to varying statistical conclusions. In this study, our primary
objective in distinguishing between Forest and NonForest is to explore the differences in postfire recovery between pixels dominated by trees and pixels dominated by grasses and shrubs. Therefore, we used the FAO definition forest maps (Qin et al. 2021).

## 2.2 | Fire Pixels

MODIS provides the MCD64A1 500-m global burned area product and the MOD14A1 1-km global active fire product. MCD64A1 has been reported to have significant omission errors (~90%) and commission errors (~50%) in the tropics (Boschetti et al. 2019). Burned area (BA) products (e.g., MCD64A1 and CCI Fire) do not align well with active fire products in Australia, primarily due to the omission of small fires (Boschetti et al. 2019). Active fire products offer absolute detection of sufficiently intense fires and identify weaker, easily missed fire spots by comparing thermal infrared and brightness temperature anomalies with surrounding pixels. The active fire product has been reported to effectively reduce fire omission errors in the tropics (Giglio et al. 2003; Justice et al. 2002; Masocha et al. 2018). Previous studies have reported that MOD14A1 can detect small fire patches in grasslands, savannas, and forests that are difficult to identify by MCD64A1 (Devineau et al. 2010; Yin 2020). Additionally, active fire products directly record high thermal radiation signals from fire-affected areas, thereby avoiding the complexities and potential errors associated with burned area algorithms. Therefore, in this study, we mainly used the 1-km MOD14A1 v6.1 daily active fire product to define fire events (Giglio and Justice 2015), whereas the 500-m MCD64A1 v6.1 (Giglio et al. 2021) and the 250-m CCI Fire v5.1 (Lizundia-Loiola et al. 2020) BA datasets served as references for cross-comparison with MOD14A1 v6.1.

Annual active fire pixel maps are generated based on the timing of the fire and the growing season (GS). The annual active fire search period was defined from July (year) to June (year + 1) to cover the entire growing season. We then examined the temporal sequence of active fire occurrences relative to the growing season, categorizing them into three types: Fire before GS, Fire within GS, and Fire after GS. Fire before GS and Fire within GS are the predominant fire types in Australia, accounting for 58% and 38% of total fires, respectively (Figure S2). These two fire types directly influenced productivity for that year; therefore, all Fires before GS and Fires within GS were included in that year's analysis. Fire after GS constitutes a small portion (4%) and primarily affects productivity in the subsequent year, thus being incorporated into the next year's analysis. These criteria generated 1-km annual active fire pixel maps from 2011 to 2019 (nine years). Fires were subsequently classified into Forest-fire (Forest) and NonForest-fire (NonForest) using the forest map (Figure S3).

Each active fire pixel for each year is considered a pixel-fire event ($f_i$). The fire frequency ($\omega_f$) of individual events was then stacked as:

$$ \omega_f = \sum_{i=1}^{n} f_i \qquad (1) $$

Where $n$ is the total number of years, $f_i=1$ if a pixel-fire event was detected in the $i$th year, and $f_i=0$ if not.

## 2.3 | Fire Severity

We calculated the 500-m and 8-day Normalized Burn Ratio (NBR), an index widely used to describe vegetation-related fire severity due to its sensitivity to green vegetation and moisture (García and Caselles 1991; Miller and Thode 2007):

$$ \text{NBR} = \frac{\rho_{\text{nir}} - \rho_{\text{swir2}}}{\rho_{\text{nir}} + \rho_{\text{swir2}}} \qquad (2) $$

Where $\rho_{\text{nir}}$ and $\rho_{\text{swir2}}$ are the surface reflectance values of near infrared (841–876 nm) and short-wave infrared (2105–2155 nm) bands from the MOD09A1 v6.1 surface reflectance product (500-m, 8-day) (Vermote 2015). We generated the NBR time series for fire events at an 8-day temporal resolution annually. Differenced NBR (dNBR) represents the absolute vegetation loss caused by the fire and is calculated as the difference between the prefire and postfire NBR:

$$ \text{dNBR} = \text{NBR}_{\text{prefire}} - \text{NBR}_{\text{postfire}} \qquad (3) $$

where $NBR_{\text{prefire}}$ is the mean of two observations (16-day) taken before the fire, whereas $NBR_{\text{postfire}}$ is the mean of two observations (16-day) taken after the fire. Given the diverse landscape types the study area covers, we used the Relative dNBR (RdNBR) to quantify fire impacts on vegetation across temporal and spatial scales (Miller and Thode 2007):

$$ \text{RdNBR} = \frac{\text{dNBR}}{\sqrt{\text{NBR}_{\text{prefire}}} \qquad (4) $$

The fire severity defined by RdNBR mainly reflects the relative change in vegetation loss before and after the fire (Figure S4). Reference values for fire severity are currently based on a literature review (Botella-Martínez and Fernández-Manso 2017; Miller and Thode 2007), where RdNBR above 0.8 is considered high, RdNBR between 0.4 and 0.8 is considered moderate, and RdNBR below 0.4 is considered low.

## 2.4 | Gross Primary Productivity Data

We used the global GPP dataset estimated by the Vegetation Photosynthesis Model (VPM) (Zhang et al. 2017). The dataset recently reported its latest version (VPM v3.0) (Meng et al. 2025; Pan et al. 2025; Pan et al. 2024), providing GPP from 2000 to 2023 (500-m). More information on the robust evaluation of VPM can be found in the Text S2. In this study, postfire recovery analyses are reported primarily using GPP from VPM in the main text. GPP from the eddy flux tower was used to evaluate the GPP from VPM (Pastorello et al. 2014). We also included three other independent GPP data sources: the MODIS GPP product (MOD17, 500-m) (Zhao et al. 2005), a statistical model using SIF (GOSIF, 5-km) (Li and Xiao 2019), and a satellite-based model using $V_{c_{max}}$ (BESS, 5-km) (Li et al. 2023), to compare the results from VPM. More details of comparisons can be found in the Text S4.
## 2.5 | Leaf Area Index Data

Leaf Area Index (LAI) data is from the MCD15A3H v6.1 LAI product (500-m, 4-day) (Myneni et al. 2015). LAI was then aggregated by annual means from July (year) to June (year +1) to match the fire years as described in Australia.

## 2.6 | Vegetation Index Data

Vegetation indices, including Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Near-Infrared Reflectance of Vegetation (NIRv), were calculated using reflectance bands from the MOD09A1 v6.1 surface reflectance product (500-m, 8-day) (Vermote 2015). EVI, NDVI, and NIRv were then aggregated by annual means from July (year) to June (year +1).

## 2.7 | Analysis of Post-Fire Recovery of GPP During 07/2000 to 06/2023

We used the time series data of fire events within individual pixels during 07/2000–06/2023 to count the number of fire events for each pixel (fire frequency, $\omega_f$), and those fire-affected pixels are grouped into two categories: single-fire pixels ($\omega_f$=1) and multiple-fire pixels ($\omega_f$>1). Time series GPP data for each fire-affected pixel are divided into two periods based on the year the fire occurred: the baseline period (prefire) and the transition period (postfire).

The baseline period was defined as spanning from 2000 to the year before the fire, ensuring a minimum of 11 years of data for reference and extending over multiple years. The GPP during the baseline period is used to represent the undisturbed state of the ecosystem. Years during the baseline period with extreme environmental conditions, such as fires, floods, or droughts, were excluded. The filtering process specifically involves (1) excluding fire years, the wettest year, and the driest year; (2) calculating the mean and standard deviation ($\sigma$) of the GPP for the remaining years and excluding any years where GPP fell outside the range of mean $\pm \sigma$; and (3) estimating the mean of the remaining years as the baseline.

The transition period is the period following the fire year, during which vegetation either transitions toward recovery or remains unrecovered. This period was generally defined as the period from the fire year until the final year of the study period (fire year—2023 in single-fire pixels), or until the next fire disturbance (fire year—next fire year in multiple-fire pixels). We used the following criteria to determine whether a pixel-fire event had recovered during the transition period:

$$ \begin{cases} \text{recovered if } t \in T (\text{GPP}_{(t)} \ge \text{GPP}_{\text{baseline}}) \\ \text{unrecovered otherwise} \end{cases} \quad (5) $$

where T represents the entire transition period, t denotes a specific year within T, and GPP(t) refers to the value of GPP for the year t. A fire event was considered recovered if GPP exceeded

the baseline in any year t within the transition period; otherwise, it was marked as unrecovered. Each year, all pixel-fire events were classified into a binary map as either recovered or unrecovered. $\omega_r$ is defined as the recovery frequency within the fire-affected pixels:

$$ \omega_r = \frac{\sum_{i=1}^{\omega_f} r_i}{\omega_f} \quad (6) $$

With $r_i=1$ if the pixel-fire event was recovered in the ith fire year and $r_i=0$ otherwise. $\omega_f$ is fire frequency. $\omega_r=1$ indicates that the pixel has recovered from all fire events in the pixel, whereas $\omega_r=0$ indicates that the pixel is unrecovered from any of the pixel-fire events. For multiple-fire pixels, $0 < \omega_r < 1$ indicates that the pixel has partially recovered.

Three metrics were calculated to quantify the postfire recovery trajectories at one pixel-fire event: relative recovery magnitude ($\varphi_{\text{rec}}$) of a pixel, relative recovery slope ($\beta_{\text{rec}}$) of a pixel, and the number of years required for recovery ($Y_{\text{rec}}$) of a pixel. Specifically:

$\varphi_{\text{rec}}$ is calculated as the percentage of GPP in the recovery year relative to the baseline during the transition period, in case of complete recovery:

$$ \varphi_{\text{rec}} = \frac{\text{GPP}_{\text{rec}}}{\text{GPP}_{\text{baseline}}} \times 100\% \quad (7) $$

Where GPP$_{rec}$ is GPP in the first year in which GPP exceeds the baseline GPP. In cases where recovery does not occur (unrecovered pixels), $\varphi_{\text{rec}}$ is defined as the percentage of the maximum GPP observed during the transition period (GPP$_{max}$) relative to the baseline:

$$ \varphi_{\text{rec}} = \frac{\text{GPP}_{\text{max}}}{\text{GPP}_{\text{baseline}}} \times 100\% \quad (8) $$

$\beta_{\text{rec}}$ is the regression coefficient (slope) of the linear regression of the annual net GPP recovery percentage relative to baseline GPP during the transition period. t is the year during the transition period:

$$ \beta_{\text{rec}} = \frac{1}{t} \frac{\text{GPP}_{t+1} - \text{GPP}_t}{\text{Baseline GPP}} \times 100\% \quad (9) $$

$Y_{\text{rec}}$ is estimated in recovered fire-affected pixels. Generally, $Y_{\text{rec}}$ ranged from 1 to 11 years. In some cases, $Y_{\text{rec}}$ equaled 0, indicating recovery occurred within the same year.

One metric was calculated to quantify the postfire recovery trajectory for all pixel-fire events in Australia: ratio of recovered pixel-fire events relative to the pixel-fire events (Ratio$_{rec}$) in Australia:

$$ \text{Ratio}_{\text{rec}} = \frac{\sum_{i=1}^{m} \omega_r \times \omega_f}{\sum_{i=1}^{m} \omega_f} \times 100\% \quad (10) $$

Where m is the count of fire-affected pixels. Ratio$_{rec}$ was calculated at single-fire pixels and multiple-fire pixels separately.
We benchmarked this framework against a recent field-based postfire recovery study at the Tumbarumba eucalypt forest site (Woodgate et al. 2025) to evaluate its robustness and quantify potential uncertainty. Our analysis at this site is highly consistent with the results of Woodgate et al. (2025), which reported that postfire recovery was high and rapid spectral recovery outpaced GPP recovery for the 2019 fire event. We also conducted the data analysis on the national scale. We found that only 10% of fire-affected pixels showed spectral recovery outpacing GPP recovery, depending on spatial scales, postfire precipitation, fire severity, and ecosystem types (Text S3). We also conducted postfire recovery analyses at three additional eddy flux tower sites (AU-Das savana site, AU-How woody savana site, and AU-Stp grassland site) that experienced fire (Figure S20). In contrast to the unrecovered forest ecosystem at Tumbarumba, the nonforest ecosystems in AU-Das, AU-How, and AU-Stp recovered to their prefire GPP levels during the study period. Notably, AU-Stp exhibited a more pronounced fire-induced instant GPP loss, yet it fully recovered within two years and showed a higher $\beta_{rec}$.

We applied this framework to each fire pixel to explore the postfire GPP recovery trajectories. For the Ratio$_{rec}$ metric, we conducted additional statistical analyses across different forest maps (Text S1), seven bioregions (Text S4), three other GPP products (MOD17, GOSIF, and BESS), and four vegetation indices reflecting canopy greenness and structure (EVI, NDVI, NIRv, and LAI) (Text S5).

## 2.8 | Analysis for Attribution of Driving Factors of Post-Fire Recovery

Australia's ecosystem productivity is predominantly driven by precipitation (Byrne et al. 2021). We explored the effects of precipitation and fire severity on recovery. Here, we used precipitation data from the ERA5-Land reanalysis climate dataset (Muñoz Sabater 2019), which has a spatial resolution of 0.1° and an hourly temporal resolution and was aggregated by annual sums spanning from July (year) to June (year + 1). Postfire precipitation was quantified by precipitation anomaly (Δ mm):

$$ \text{Precipitation anomaly} = \overline{p_{\text{max}}} - \overline{p_{\text{mean}}} \quad (11) $$

For a recovered fire event, $\overline{p_{max}}$ is defined as the maximum annual precipitation from the fire year to the recovered year. For an unrecovered fire event, $\overline{p_{max}}$ is the maximum annual precipitation for the entire transition period. $\overline{p_{mean}}$ is the multi-year mean annual precipitation from the year 2000 to the fire year.

All fire events were then categorized into subgroups based on different intervals of precipitation anomalies and fire severity (RdNBR). Subgroups with sufficient fire events were selected to ensure statistical significance. For the All and NonForest categories, subgroups with at least 1000 fire events were selected, whereas subgroups with at least 100 fire events were selected for the Forest category.

$\text{Ratio}_{\text{rec}}$, $\varphi_{\text{rec}}$, and $\beta_{\text{rec}}$ were calculated within each subgroup. In each subgroup, Ratio$_{rec}$ was calculated using Equation (10),

whereas $\varphi_{\text{rec}}$ and $\beta_{\text{rec}}$ were calculated as the mean values of all fire-affected pixels within the subgroup. We then used the logarithmic model to explore the relationship between Ratio$_{rec}$, $\varphi_{\text{rec}}$, and $\beta_{\text{rec}}$ with precipitation anomalies under various fire severities and calculated the significance.

For the recovered fire event, the timing and amount of precipitation may impact $Y_{\text{rec}}$. Here, we used an index to quantify the temporal distribution of precipitation, the Normalized Precipitation Timing Index (NPTI), which integrates the temporal variation characteristics of postfire precipitation (Refer to the Text S6 for more details on the calculation and methodology of NPTI). Higher NPTI indicates earlier and more substantial postfire precipitation. Similarly, we categorized all recovered fire events from all years into multiple subgroups based on NPTI and RdNBR intervals and calculated the mean $Y_{\text{rec}}$ within each subgroup.

## 2.9 | Impacts of Fire on Interannual GPP Change

We explored the components of interannual GPP change (ΔGPP) over the long term (from 2000 to 2022). ΔGPP was calculated as the difference between year t and year t-1. For each fire-affected pixel, ΔGPP can be decomposed into four subcategories:

$$ \Delta \text{GPP} = \Delta \text{GPP}_{\text{NN}} + \Delta \text{GPP}_{\text{FN}} + \Delta \text{GPP}_{\text{NF}} + \Delta \text{GPP}_{\text{FF}} \quad (12) $$

where ΔGPP$_{NN}$ refers to the ΔGPP of the fire occurred in consecutive years (year t and year t-1) with no fire, driven primarily by climate change and land use change. ΔGPP$_{FN}$ refers to the ΔGPP of the fire occurred in the year t-1, representing the annual climate-induced GPP recovery. ΔGPP$_{NF}$ refers to the ΔGPP of the fire occurred in the year t, and ΔGPP$_{FF}$ refers to the ΔGPP of the fire occurred in consecutive years (year t and year t-1). ΔGPP$_{NF}$ and ΔGPP$_{FF}$ are fire-induced GPP losses. We selected a representative pixel to illustrate the analytical process (Figure S30).

The contribution of each subcategory (| ΔGPP$_{i}$ |) to the total ΔGPP is estimated as:

$$ \text{Contribution to } \Delta \text{GPP} = \frac{|\Delta \text{GPP}_i|}{\sum_i |\Delta \text{GPP}_i|} \times 100\% \quad (13) $$

Here, we quantified the contribution of ΔGPP$_{i}$ on ΔGPP at fire-affected pixels and the contribution of ΔGPP$_{i}$ on ΔGPP at the entire Australian.

# 3 | Results

## 3.1 | Characteristics of Fires in Australia During 2011–2019

Observations of active fire from satellite products (MOD14A1, 1-km resolution) reveal that from 2011 to 2019, $1.7 \times 10^6$ pixels ($1.5 \times 10^6$ km$^2$) were affected by active fires, covering 19% of Australia's land area (Figure 1a,b). We found that $1.3 \times 10^6$ pixels ($1.2 \times 10^6$ km$^2$) experienced only one fire event since 2011 (single-fire pixels), distributed over temperate forests and
<figure><img src="image_2.png" /><figcaption>(b)</figcaption></figure>

<figure><img src="image_3.png" /><figcaption>(c)</figcaption></figure>

<figure><img src="image_4.png" /><figcaption>(d)</figcaption></figure>

<figure><img src="image_5.png" /></figure>

<figure><img src="image_6.png" /><figcaption>**FIGURE 1** | Fire pixels and events in Australia from 2011 to 2019. (a) Show the spatial distribution of single-fire pixels ($\omega_f$=1) and multiple-fire pixels ($\omega_f$>1) (1-km) during the study period. (b) Summarizes the fire events and proportion of fire pixels for Forest, NonForest, and all (Forest and NonForest) along the latitude gradient in 0.1° bins. The forest map was developed by Qin et al. (2021). (c) Reports the interannual variability in pixel-fire events and the corresponding proportion of Australia's land area for NonForest and Forest. (d) Reports the fire severity (RdNBR) for pixel-fire events in single-fire pixels ($\omega_f$=1) and in multiple-fire pixels ($\omega_f$>1). Severity categories: Low (RdNBR $\le$0.4), moderate (0.4< RdNBR $\le$0.8), and High (RdNBR >0.8). Black solid dots and white solid lines are the mean and median values in boxplot. The numbers in the legend are the mean values and interquartile range (mean [interquartile range]). Map lines delineate study areas and do not necessarily depict accepted national boundaries.</figcaption></figure>

savanna fire-prone zones, covering 15% of Australia's land area. Of these, the number of extreme fire pixels in 2019 was $0.2 \times 10^6$. Conversely, $0.4 \times 10^6$ pixels ($0.3 \times 10^6$ km$^2$) experienced two or more fire events (multiple-fire pixels), primarily within the savanna fire-prone biome, covering 4% of Australia's land area. The annual fire-affected pixels (or area) varied across years, ranging from $0.1 \times 10^6$ ($0.1 \times 10^6$ km$^2$) to $0.4 \times 10^6$ pixels ($0.4 \times 10^6$ km$^2$) during the study period, covering 2%–5% of Australia's land area (Figure 1c and Table S4). Nonforest fires (NonForest), including savannas, shrublands, and grasslands, were dominant, accounting for 94% of total fire-affected pixels and are the primary contributor to the interannual variability of burned areas over the years (Figure 1c). Forest-fire (Forest) comprised only 6% of total fire-affected pixels and had small interannual variability except for the extreme southeastern “Black Summer” forest fires of 2019 (Figure 1c).

Fire severity, defined by the RdNBR, shows a gradient from low to high in NonForest pixels, and a gradient from low to moderate in Forest (Figure 1d). Higher RdNBR in NonForest indicates that fires generally cause more severe damage to nonforests than forests. 81% of NonForest had minimum daily GPP after fire below $1 \text{ G} \text{C} \text{m}^{-2} \text{day}^{-1}$ (Figure S5d), which means that NonForest lost its photosynthetic capacity just after fires. Although Forest experienced higher daily GPP instant losses

after fires (Figure S5b), the minimum daily GPP after fire averages $2.2 \text{ G} \text{C} \text{m}^{-2} \text{day}^{-1}$ (Figure S5d), suggesting that fires did not kill all the trees, and these pixels retained a limited amount of photosynthetic activity.

## 3.2 | PostFire Recovery in Single-Fire Pixels (1-km Spatial Resolution)

Of the $1.3 \times 10^6$ single-fire pixels ($1.2 \times 10^6$ km$^2$), the postfire recovery ratio ($Ratio_{rec}$) is 88% (Figure 2a,b), indicating that 88% of pixel-fire events recovered to prefire level and are distributed across the entire latitudinal gradient. 12% of pixel-fire events have remained unrecovered yet and are predominantly distributed in central and southwestern Australia's arid desert regions. Both Forest and NonForest show high $Ratio_{rec}$, with the NonForest recovery ($Ratio_{NonForest}$=88%) being slightly higher than the Forest recovery ($Ratio_{Forest}$=83%). The average $Y_{rec}$ of single-fire pixels is 2.3 years (Figure 2c). $Y_{rec}$ varied across years, with a general pattern of shorter $Y_{rec}$ for NonForest than Forest (Figure S6). Exceptions, such as the rapid recovery observed in Forest in 2019, resulted in the overall average $Y_{rec}$ for Forest (2.2 years) and NonForest (2.3 years) being close to each other. The mean and range of relative recovery magnitude ($\phi_{rec}$) for single-fire pixels are 135% [110%, 156%], showing a widespread
<figure><img src="image_7.png" /><figcaption>**FIGURE 2** | Postfire recovery of single-fire pixels. (a) Show the spatial distribution of unrecovered and recovered fire-affected pixels. (b) Summarizes the counts of fire-affected pixels and proportion of unrecovered and recovered pixel-fire events for Forest, NonForest, and all (Forest and NonForest) along the latitude gradient in 0.1° bins. The forest map was developed by Qin et al. (2021). (c) Shows the proportion of pixels with different $Y_{rec}$ values among all fire-affected pixels. (d, e) Show the distribution of the GPP recovery magnitude in pixels with recovery magnitude ($\varphi_{rec}$) and the recovery slope ($\beta_{rec}$) for Forest, NonForest, and All. Black solid dots and white solid lines are the mean and median values in boxplot. The numbers in the legend are the mean values and interquartile range (mean [interquartile range]). Map lines delineate study areas and do not necessarily depict accepted national boundaries.</figcaption></figure>

pattern of overcompensation (Figure 2d). Specifically, NonForest has a higher mean value and broader range of $\varphi_{rec}$ (138% [111%, 160%]) than Forest (110% [103%, 117%]), showing more dramatic GPP rebounds consistently observed across all years (Figure S7). The mean and range of relative recovery slope ($\beta_{rec}$) for single-fire pixels are 8% [2%, 13%]. Forest (10% [4%, 15%]) was slightly higher than NonForest (8% [2%, 12%]) (Figure 2e), primarily due to the high $\beta_{rec}$ in Forest in 2019 as well. However, in most years, the $\beta_{rec}$ in NonForest was higher than in Forest (Figure S8). It is noteworthy that in the 12% of pixel-fire events have remained unrecovered, 10% of pixel-fire events have $\beta_{rec}$ very low ($\beta_{rec} < 1$%), indicating that these unrecovered areas have experienced degradation, whereas 2% of pixel-fire events are due to without enough time to recover.

## 3.3 | PostFire Recovery in Multiple-Fire Pixels (1-km Spatial Resolution)

Of the $0.4 \times 10^6$ multiple-fire pixels (or $0.3 \times 10^6$ km$^2$), 71% fully recovered, 1% remained unrecovered, and 27% partially recovered (Figure 3a,b). Using Equation (10), the average $Ratio_{rec}$ was 86%, which is only 2% lower than that in single-fire pixels. The NonForest recovery ratio ($Ratio_{NonForest}^{rec} = 86\%$) is higher than

that of Forest ($Ratio_{rec}^{Forest} = 80\%$). Notably, under similar $Ratio_{rec}$, multiple-fire pixels had notably shorter $Y_{rec}$ (1.2 years) than single-fire pixels (2.3 years) (Figures 2c and 3c). NonForest has shorter $Y_{rec}$ than Forest, consistent with observations in single-fire pixels (Figures 3c and S9). The mean and range of $\varphi_{rec}$ for multiple-fire pixels show overcompensation (115% [105%, 123%]) as well but lower than in single-fire pixels (135% [110%, 156%]) (Figures 2d, 3d, S7, and S10), indicating that increased fire frequency negatively impacts the recovery magnitude in both Forest and NonForest. The $\beta_{rec}$ shows interesting patterns in multiple-fire pixels (Figures 2e and 3e). $\beta_{rec}$ in multiple-fire pixels was slightly higher than in single-fire pixels across all years (Figures S8 and S11). The higher recovery slope highlights the adaptability (i.e., potential to adapt) to high-frequency fires.

To verify the robustness of our results from MOD14A1 v6.1 and VPM GPP, we repeated the postfire recovery analysis with vegetation proxies, including three other GPP products (MOD17, GOSIF, BESS) and four vegetation index products (EVI, NDVI, NIRv, LAI), from multiple satellite-derived products (Text S5). We also repeated the postfire recovery analysis by pairing each vegetation proxy with three fire products (MOD14A1, MCD64A1, CCI) to undertake the postfire recovery analyses. Our results show that the postfire recovery patterns from
<figure><img src="image_8.png" /><figcaption>**FIGURE 3** | Postfire recovery of multiple-fire pixels. (a) Show the spatial distribution of unrecovered, partially recovered, and fully recovered fire-affected pixels. (b) Summarizes the counts of fire-affected pixels and proportion of unrecovered, partially recovered, and fully recovered pixel-fire events for Forest, NonForest, and all (Forest and NonForest) along the latitude gradient in 0.1° bins. The forest map was developed by Qin et al. (2021). (c) Shows the proportion of pixels with different $Y_{rec}$ values among all fire-affected pixels. (d, e) Show the distribution of the GPP recovery magnitude in pixels with recovery magnitude ($\varphi_{rec}$) and the recovery slope ($\beta_{rec}$) for Forest, NonForest, and All. Black solid dots and white solid lines are the mean and median values in boxplot. The numbers in the legend are the mean values and interquartile range (mean [interquartile range]). Map lines delineate study areas and do not necessarily depict accepted national boundaries.</figcaption></figure>

multiple satellite-derived products are highly consistent across all combinations of vegetation proxies and fire products, which provide compelling evidence of Australia's strong and rapid postfire recovery.

## 3.4 | Impacts of Precipitation on Post-Fire Recovery Under Various Levels of Fire Severity

Most ecosystems in Australia are water-limited, making postfire recovery closely linked to water availability and fire severity. Across the gradient of fire severity, we applied a logarithmic regression model ($y = a\log(x) + b$) to explore the relationship between postfire recovery (Ratio$_{rec}$) and precipitation. The overall response of Ratio$_{rec}$ to precipitation (Δmm) exhibited a significant positive correlation, characterized by an initial increase in Ratio$_{rec}$ with rising precipitation and a saturation above a specific threshold (Figure 4a–c). Accordingly, the response of Ratio$_{rec}$ to precipitation can be divided into two stages: rapid increase and saturation. In the rapid increase stage, Ratio$_{rec}$ gradually increases with precipitation but levels off once the response to precipitation reaches saturation. During this stage, fire severity and precipitation jointly influence the Ratio$_{rec}$, with fire severity impacting

negatively and precipitation impacting positively. In the saturation regime, Ratio$_{rec}$ remains stable at its peak, indicating that additional precipitation is no longer necessary and that recovery can proceed irrespective of fire severity. Notably, the saturation point for NonForest is relatively low, Ratio$_{rec}$ reaching peak at Δmm > 50 mm, whereas it is higher for Forest, Ratio$_{rec}$ reaching peak at Δmm > 150 mm.

The response mechanism of Ratio$_{rec}$ to precipitation is fundamentally driven by the recovery magnitude ($\varphi_{rec}$) and slope ($\beta_{rec}$) (Figure 4d–i). Similarly, the logarithmic model indicates a significant positive correlation between $\varphi_{rec}$, $\beta_{rec}$, and precipitation. After precipitation saturation, excessive precipitation tends to slightly weaken or halt this effect, but it still maintains relatively high levels, keeping Ratio$_{rec}$ at its peak. This response pattern is more pronounced in NonForest than in Forest, highlighting the rapid rebound of NonForest and its sensitivity to water availability. The responses of $\varphi_{rec}$ and $\beta_{rec}$ to precipitation are particularly noteworthy under varying fire severities. Fire severity positively correlates with $\varphi_{rec}$ and $\beta_{rec}$ under wet conditions (Δmm > 0). This indicates that when water is sufficient, larger fire-induced damage leads to higher recovery magnitudes and slopes. These findings reveal the high adaptability of Australia's ecosystems to fire and the dominant role of precipitation in their recovery.
<figure><img src="image_9.png" /><figcaption>**FIGURE 4** | The relationship between postfire recovery trajectories and precipitation varies under different levels of fire severity. Postfire precipitation was quantified using precipitation anomalies (Δ mm), and fire severity was quantified using RdNBR. Each point represents the mean value in subgroup (see Methods). Each panel in (a–i) presents statistical metrics under varying precipitation anomaly and fire severity: Ratio<sub>rec</sub> (a–c), recovery magnitude (φ<sub>rec</sub>) (d–f), and recovery slope (β<sub>rec</sub>) (g–i). (j–l) show Y<sub>rec</sub> under different Normalized Precipitation Timing Index (NPTI) and fire severity (see Methods). Three columns correspond to Forest, NonForest, and All, respectively. The curves are the fitted regression line of the logarithmic model (y = alog(x) + b), with significance determined based on the F-test. *p* < 0.01 (***), and *p* < 0.05 (*) indicate confidence levels of 99% and 95%, respectively. The forest map was developed by Qin et al. (2021).</figcaption></figure>

In recovered fire pixels, we further explored Y<sub>rec</sub> with factors associated with precipitation amount and timing (Figure 4j–l). We used the Normalized Precipitation Timing Index (NPTI) as an indicator to simulate scenarios with varying precipitation amounts and timings. NPTI is generally negatively correlated with Y<sub>rec</sub> and positively correlated with RdNBR, which means earlier and greater precipitation results in shorter recovery times, whereas higher fire severity prolongs recovery times. This pattern is clear and direct in NonForest but not significant in Forest, although Forest exhibits a similar overall trend. It is important to clarify that, although fire severity negatively impacts Y<sub>rec</sub>, this does not conflict with the positive correlation of fire severity with φ<sub>rec</sub> and β<sub>rec</sub>. Higher fire severity is typically associated with greater GPP loss, making a longer Y<sub>rec</sub> reasonable.

**3.5 | Impacts of Fire on Interannual GPP Change**

We extended the GPP data to cover the period from 2000 to 2022 to explore the contribution of fire-related GPP to the long-term interannual GPP change (ΔGPP). Within fire-affected pixels, single-fire pixels, ΔGPP<sub>NN</sub> dominated ΔGPP, accounting for 89% [85%–94%] of ΔGPP. ΔGPP<sub>FN</sub> accounted for 6% [3%, 9%] of ΔGPP, and ΔGPP<sub>NF</sub> accounted for 5% [2%, 7%] (Figure 5a). For multiple-fire pixels, we merged the ΔGPP<sub>NF</sub> and ΔGPP<sub>FF</sub> into one category, as both are related to fire-induced GPP loss (Figure 5b). Multiple-fire pixels exhibited similar patterns to single-fire pixels, but with a relatively larger contribution of fire to ΔGPP. ΔGPP<sub>NN</sub>, ΔGPP<sub>FN</sub>, and ΔGPP<sub>NF</sub> + ΔGPP<sub>FF</sub> accounted for 77% [70%, 87%], 12% [6%, 16%], and 11% [5%, 15%] of ΔGPP.
<figure><img src="image_10.png" /><figcaption>**FIGURE 5** | Interannual GPP change (ΔGPP) across fire-affected pixels and whole Australia. ΔGPP is defined as: $ΔGPP_{(t)} = GPP_{(t)} - GPP_{(t-1)}$, ΔGPP is divided into four subcategories: ΔGPP = $ΔGPP_{NN} + ΔGPP_{FN} + ΔGPP_{NF} + ΔGPP_{FF}$. $ΔGPP_{NN}$ refers to the ΔGPP of the year with no fire, driven primarily by climate change and land use change. $ΔGPP_{FN}$ refers to the ΔGPP of the year fire that occurred in the previous year, representing the annual climate-induced GPP recovery. $ΔGPP_{NF}$ refers to the ΔGPP of the years with fire, and $ΔGPP_{FF}$ refers to the ΔGPP of the years with fire occurring in consecutive years. (a, b) Show the distributions of each subcategory contribution to ΔGPP for fire-affected pixels, with (a) for single-fire pixels and (b) for multiple-fire pixels. (c) shows the annual time series of Australia's total ΔGPP. Each bar is stacked by the sum of ΔGPP across all pixels in that subcategory. The positive and negative signs of ΔGPP represent increases and decreases in interannual anomalies. (d) Shows the contribution of each subcategory to total Australia's interannual GPP difference by mean and 95% confidence interval (mean±95% CI) across all years.</figcaption></figure>

We then extended the analysis to the entire Australia, exploring the contribution of total fire-related GPP to the continent's total GPP. Over the two decades, total ΔGPP in Australia ranged from -1.07 GtC year⁻¹ to 1.12 GtC year⁻¹ (Figure 5c). ΔGPP<sub>FN</sub> ranged from -0.95 GtC year⁻¹ to 1.08 GtC year⁻¹ and served as the primary contributors to the ΔGPP, explaining 82% [74%, 90%] of the ΔGPP over the years (Figure 5d). ΔGPP<sub>FN</sub> ranged from -0.01 GtC year⁻¹ to 0.10 GtC year⁻¹, with most of the years showing positive values, explaining 11% [5%, 17%] of the ΔGPP over the years. ΔGPP<sub>NF</sub> + ΔGPP<sub>FF</sub> ranged from -0.07 GtC year⁻¹ to 0.01 GtC year⁻¹, explaining 7% [4%, 10%] of the ΔGPP over years. ΔGPP<sub>NF</sub> + ΔGPP<sub>FF</sub> were generally negative as fires directly reduce GPP. However, in some years, such as 2003, 2010, and 2020, they turned positive, reflecting climate-driven recovery within the year.

Fire-related GPP loss ($ΔGPP_{NF} + ΔGPP_{FF}$) accounts for 7% of ΔGPP (Figure 5d). This proportion is non-negligible, as the fire-affected areas are much smaller than the areas not affected by fire. The key point is that the annual climate-driven GPP recovery (11%) can fully offset the fire-related GPP loss, a similar pattern is also observed at the pixel scale (Figure 5a,b). These findings suggest that the long-term impact of fires on ΔGPP in

Australia is significantly smaller than that of climate change or land-use changes, primarily due to the offset effect of rapid post-fire recovery.

## 4 | Discussion

As shown by the postfire recovery dynamics in GPP (together with multiple satellite-derived products) across $1.7 \times 10^6$ fire-affected pixels ($1.5 \times 10^6$ km²) from 2011 to 2019, Australian ecosystems demonstrate strong and rapid postfire recovery in the carbon uptake capacity in response to fire disturbances. This recovery is characterized by high recovery ratios, overcompensation, and rapid recovery. NonForest ecosystems are worth highlighting as they exhibit an extraordinarily strong and rapid recovery. This highlights NonForest in Australia as fire-adapted ecosystems, attributed to the rapid resprouting of herbaceous and shrub species, along with their lower resource requirements (Driscoll et al. 2024; Xu et al. 2024). Note that we did not differentiate between grasslands, shrubs, and savannas here, as savannas are typically composed of grass and trees. Further refinement is needed to distinguish between understory and overstory fires in the future, as savannas, which involve a more
dominant grass layer, are expected to recover more quickly. Forest is relatively stable, as indicated by recovery metrics showing narrower response ranges to fire (Woodgate et al. 2025). Most forest species have adapted to fire over millions of years, with the dominant eucalyptus species in Australia having evolved fire resilience by vigorously resprouting after fire or by releasing large quantities of seeds that rapidly germinate after burning (Woodgate et al. 2025). The stronger and more rapid postfire recovery observed in NonForest compared to Forest could be attributed to the higher fire-induced GPP instant loss in forests (Figure S5b), as well as the higher postfire demands of radiation, water, and nutrients (Bond and Midgley 2001; Crisp et al. 2011).

With these traits that promote resilience, we find that strong and rapid postfire recovery is largely driven by subsequent precipitation and fire severity. Consistent with previous studies, precipitation in water-limited Australian ecosystems is the dominant factor driving postfire GPP recovery (Byrne et al. 2021), as it provides essential water resources for resprouting and germination, while also accelerating soil nutrient cycling (Li et al. 2009). Precipitation directly determines whether vegetation can recover and shape the recovery trajectories. The spatial patterns of postfire recovery indicate that unrecovered fire pixels were concentrated in arid areas. In these regions, insufficient precipitation and high fire severity fail to counterbalance fire-induced soil warming and reduce soil moisture (Baur et al. 2024; Zhao et al. 2024), resulting in ecosystem degradation or desertification (Armstrong McKay et al. 2022; Driscoll et al. 2024; Feng et al. 2021). We checked several representative fire-affected areas, and pixels with below-average precipitation for the region were more likely to remain unrecovered (Figure S29). One key finding is that the precipitation threshold required to support postfire recovery is relatively modest—in most cases, slightly exceeding the long-term mean precipitation is sufficient to facilitate recovery (Figure 4). This finding leads to the conclusion that ecosystems in Australia are highly sensitive to water availability, which is particularly significant for Australia, given that its ecosystems are predominantly arid and semi-arid. When precipitation is sufficient, vegetation can recover from any fire event. A prime example is the extensive southeastern forest fires in 2019, where early and abundant postfire precipitation greatly accelerated recovery (Qin et al. 2022). The high $\beta_{rec}$ observed in forests during 2019 strongly supports this conclusion (Figure S8). Moreover, it is easy to understand that earlier and higher precipitation would shorten recovery times. However, the complex response pattern of Forest to NPTI may arise from variations in soil types, which could influence how precipitation impacts the ecosystem (Fernandez-Going et al. 2012). Additionally, early precipitation may trigger microbial flushes, altering soil microbial diversity and functionality (DeBano and Conrad 1978), which warrants further investigation.

Fire influences postfire recovery in GPP in two ways: fire frequency and fire severity. In general, an increasing fire frequency negatively affects postfire GPP recovery, most directly by reducing the level of recovery and shortening the time window for recovery. This prevents ecosystems from fully regaining their maximum carbon uptake capacity. On the positive side, our results indicate that these increases in fire frequency have a

minimal effect on the postfire recovery ratio. Most vegetation in Australia, especially NonForest, can still return to baseline levels despite repeated fire disturbances. However, as fire frequency has increased over the recent decades and intervals between fires continue to shorten, the negative impacts are likely to intensify.

Fire severity reveals an interesting pattern: under wet conditions, vegetation affected by higher fire severity exhibits a stronger recovery response, i.e., a higher level of GPP recovery than prefire and steeper slopes. Our findings also align with a previous grassland study, which reported a 30%-60% increase in productivity following fire (Santos et al. 2003). In our analyses, productivity increased by 38% for single-fire events and 15% for multiple-fire events. Our results also corroborate a North American boreal fire study, which reported an 8%-40% increase in SIF following fires (Kim et al. 2024). This phenomenon can be attributed to several complementary mechanisms. First, intense burning returns the nutrients in dead plant matter to the soil, providing a short-term pulse of nutrient inputs or a fertilization effect (Caon et al. 2014; Certini 2005). Second, the removal of preexisting plant species creates space that reduces competition for fire-adapted or pioneer species (Driscoll et al. 2024; Xu et al. 2024), such as many eucalypts and shrubs, which can rapidly occupy (Clarke and Knox 2002; Crisp et al. 2011). Third, these fire-adapted species possess strong resprouting and germination capacities (Knox and Clarke 2006), as well as high photosynthetic efficiencies (Clemente et al. 2005). Finally, large postfire precipitation maximizes the positive effects from nutrient release and reduced competition (Blanco-Rodríguez et al. 2023; Davis et al. 2023), thereby producing the high recovery magnitudes and slopes after fires. In comparison, under drought conditions, fire severity exerts a generally negative influence. These findings indicate fire regulates and constrains recovery processes by altering initial conditions. However, the high sensitivity of vegetation to water drives strong resilience and adaptability in response to fire impacts.

Australia's ecosystems are mainly composed of water-limited and fire-dependent ecosystems (Byrne et al. 2021), which guided our continental-scale postfire recovery analysis focusing on precipitation and fire severity. More potential drivers need to be considered in the future to conduct postfire recovery analysis at a finer scale (Beringer et al. 2015). For example, as mentioned, in grassland and savanna fire zones, intense fires can trigger rapid species turnover, with pioneer fire-adapted species supplanting preexisting species (Driscoll et al. 2024; Xu et al. 2024), and in extreme cases, driving ecosystem extinction and changes in land use (Armstrong McKay et al. 2022; Driscoll et al. 2024; Feng et al. 2021). In relatively complex forest ecosystems, such as those in the southeastern Australian eucalypt forests, radiation is as critical a driver of postfire recovery as precipitation (Woodgate et al. 2025). Furthermore, forest ecosystems demand attention to vertical structural complexity. Evidence from a recent study suggests that the overstorey and understory follow distinct postfire recovery trajectories, with a substantial loss of overstorey biomass and a concurrent increase in understory biomass (Woodgate et al. 2025), a pattern that optical sensors cannot capture accurately and that underscores the need to incorporate active remote-sensing datasets capable of resolving
three-dimensional canopy structure, such as GEDI LiDAR (Potapov et al. 2021). Moreover, biophysical interactions, such as hydrothermal coupling and soil microbial community dynamics, also modulate postfire recovery trajectories and should be incorporated into analyses at finer scales (Certini 2005; DeBano 2000).

Because of this strong and rapid postfire recovery, the fire-induced GPP losses are unlikely to have solely long-term effects on the interannual GPP difference, thereby explaining the previously observed limited impacts of fire on the interannual variability of biomass and carbon sinks (Beringer et al. 2007; Qin et al. 2022). Even during the devastating forest fires of 2019, the strong and rapid precipitation-induced intra-annual recovery (Figure S8) resulted in the GPP losses from that year's fires not being significant at the interannual scale. In the subsequent three years (2020–2022), GPP continued to increase significantly due to above mean rainfall, particularly in Forest. This evidence suggests that vegetation recovery might have reabsorbed a large fraction or the totality of the 2019 forest emissions from burned biomass, contrary to what some studies had suggested (Abram et al. 2021; van der Velde et al. 2021). Furthermore, these findings help explain a recent study on Australia's carbon budget, where net fire emissions (9.9 TtC yr⁻¹) are significantly lower than total gross fire emissions (158 TtC yr⁻¹) (Villalobos et al. 2023).

Other work tracking aboveground biomass change after fires suggests that the impact of fires on Australia's living carbon stock currently remains within the bounds of ecosystem self-repair (Byrne et al. 2021; Qin et al. 2022). We acknowledge that our study and those cited above are restricted to post-fire vegetation carbon uptake dynamics and do not quantify other fire-induced carbon fluxes, including the combustion and respiration of carbon in soils and litter, as well as increased erosion and carbon transport into inland waters and coastal zones.

We also note that increased fire frequency in forests and total burned area in the whole of Australia has already been observed over the past three decades (Canadell et al. 2021). With a warming climate and rainfall becoming more variable, Earth System models suggest an increase in fire intensity and a lengthening of the fire season in many regions of the world, including Australia (Jones et al. 2022). We acknowledge that our results are based on a decade of fires and GPP postfire recovery in a country where the climate and fire occurrence are highly variable in time frames of years to decades.

Future research can further investigate the role of this GPP recovery pattern in atmospheric CO₂ dynamics to comprehensively quantify the impact of fires on carbon dioxide anomalies in Australia. That will need to include the incorporation of postfire recovery into simulations of Earth system models (e.g., CESM's CLM5, ORCHIDEE, LP-GUESS, JSBACH) (Lawrence et al. 2019; Yue et al. 2014), as we found that a large portion of fire pixels recover their GPP within a year, which may lead to models overestimating fire-induced losses if they ignore such a fast recovery process. Other processes, such as full biomass and soil carbon dynamics, and the ultimate fate of carbon transported into inland water bodies and coastal zones, are also needed.

# 5 | Summary

This study systematically analyzed Australia's postfire GPP recovery trajectories of all fires from 2011 to 2019. The findings reveal a strong and rapid postfire recovery in Australian ecosystems, highlighting the GPP resilience and adaptability to fire disturbances. Climate intensification is expected to exacerbate fires and heighten the risk of terrestrial carbon sink loss, making it essential to deepen understanding of this climate-driven postfire recovery (Burton et al. 2024; Stevens-Rumann et al. 2018). Our results provide new insights into the long-term impacts of fire on ecosystem functionality and carbon anomaly in Australia. Future research should further investigate the dynamics of biomass and soil carbon dynamics, the role of land-use changes, hydrothermal coupling, soil microbial dynamics, and shifts in ecosystem structure as driving factors to comprehensively assess the potential mechanisms of this recovery pattern and the associated risks of carbon loss under future warming scenarios.

## Author Contributions

**Li Pan:** conceptualization, data curation, formal analysis, investigation, methodology, visualization, writing – original draft, writing – review and editing. **Xiangming Xiao:** conceptualization, data curation, formal analysis, funding acquisition, investigation, methodology, project administration, resources, supervision, writing – review and editing. **Yuanwei Qin:** conceptualization, data curation, formal analysis, methodology, supervision, writing – review and editing. **Josep G. Canadell:** conceptualization, formal analysis, investigation, supervision, writing – review and editing. **Alfredo Huete:** conceptualization, formal analysis, investigation, supervision, writing – review and editing. **Philippe Ciais:** conceptualization, formal analysis, investigation, methodology, supervision, writing – review and editing. **Shenglai Yin:** investigation, validation, writing – review and editing. **Chenchen Zhang:** methodology, validation, writing – review and editing. **Baihong Pan:** methodology, validation, writing – review and editing. **Chenglong Yin:** writing – review and editing. **Cheng Meng:** writing – review and editing. **Yuan Yao:** writing – review and editing. **Haoming Xia:** investigation, writing – review and editing.

## Acknowledgements

This work was supported by the research grants from the U.S. National Science Foundation (OIA-1946093) and NASA (80NSSC24K0118). The authors declare no competing interests. We thank the reviewers for their time and constructive comments, which helped improve this work.

## Conflicts of Interest

The authors declare no conflicts of interest.

## Data Availability Statement

The data that support the findings of this study are openly available in Figshare at https://doi.org/10.6084/m9.figshare.29318816.v2. MODIS data was obtained from the NASA Land Processes Distributed Active Archive Center at https://doi.org/10.5067/MODIS/MCD64A1.061, https://doi.org/10.5067/MODIS/MOD14A1.006, https://doi.org/10.5067/MODIS/MCD15A3H.061 and https://doi.org/10.5067/MODIS/MOD09A1.061. CCI Fire v5.1 data was obtained from Centre for Environmental Data Analysis at https://doi.org/10.5285/58f00d8814064b79a0c49662ad3af537. VPM GPP data was obtained from Data Publisher for Earth & Environmental Science at https://doi.org/10.1594/PANGAEA.879560. ERA5-Land reanalysis data was obtained
from Copernicus Climate Change Service Climate Data Store at https://doi.org/10.24381/cds.e2161bac.

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## Supporting Information

Additional supporting information can be found online in the Supporting Information section.