Our model data come from two global atmospheric composition reanalyses, CAMSRA and MERRA-2, whose main characteristics are summarized in Table . The reanalyses are evaluated against surface in situ measurements obtained from two European Environment Agency (EEA) databases, AirBase, for the period 2003–2012 , and AQ e-Reporting , for the period 2012–2020. No significant inconsistencies are expected between AirBase and AQ e-Reporting given that stations included in both databases are obtained from the same network. Though stations may be renamed, relocated or even removed with time, this is not expected to significantly affect our data given the large number of stations considered and the continuous addition of new stations into the network throughout the whole period of 2003–2020.

Our domain of study extends from 25∘ W to 45∘ E in longitude and from 27 to 72∘ N in latitude, thus covering all of continental Europe, as well as the Canary Islands, Iceland, western/European Russia, North Africa, and the westernmost regions of the Middle East and the Caucasus. For convenience, both CAMSRA and MERRA-2 are regridded over this domain on a common regular longitude–latitude grid at a resolution of 0.2∘ × 0.2∘ (roughly 20 km) through bilinear interpolation. The (pointwise) observations are also gridded to this same resolution by averaging (at daily scale) all the stations available within a given grid cell. Compared to a pointwise-to-gridded comparison, this is expected to partly overcome the issues of spatial representativeness and spatial heterogeneity, although we acknowledge here that more sophisticated methods such as those proposed by (which employ geostatistical approaches by making use of semivariograms and kriging) might be worth implementing in the future. However, when considering only rural, rural–regional and rural–remote background stations, the proportion of gridded daily observations based on one single daily observation (two daily observations) is 96.1 % (3.5 %) for NO2, 95.4 % (4.4 %) for O3, 96.7 % (3.2 %) for SO2, 97.9 % (1.9 %) for CO, 91.0 % (8.5 %) for PM10 and 92.5 % (7.4 %) for (7.4 %) for PM2.5; these high percentages are explained by the presence of numerous missing values throughout the period of study. Table  and Fig.  provide some information on the observations available over our European domain during 2003–2020, in terms of both pointwise and gridded observations (the total number of observations is typically reduced by a 2–3 factor after the gridding operation). Unfortunately, in situ observations from GHOST are not available for several countries falling within the domain considered in this study, located in North Africa (e.g. Morocco, Algeria, Tunis, Libya, Egypt), Eastern Europe (e.g. Russia, Belarus, Ukraine) and the Middle East (e.g. Israel, Lebanon, Jordan, Syria), thus somewhat limiting the scope of the evaluation, particularly in terms of spatial variability and pollution hotspots.

The evaluation is performed on a set of metrics including the (normalized) mean bias ((n)MB), the (normalized) root mean square error ((n)RMSE) and the Pearson correlation coefficient (PCC), defined as follows: 3aMB=1N∑i=1N(mi-oi),3bnMB=MBo‾×100%,3cRMSE=∑i=1N(mi-oi)2N,3dnRMSE=RMSEo‾×100%,3ePCC=1N-1∑i=1N(mi-m‾)(oi-o‾)σmσo, where mi and oi are the predicted and observed concentrations, m‾ and o‾ are their means, σm and σo are their standard deviations, and N is the number of points employed to compute the statistics (i.e. number of daily values across all stations). The index i accumulates over time (e.g. daily, monthly) at each station (i.e. gridded cell with available observations). The final value for each statistic is obtained by taking the median across all stations. The overlines in Eq. ()–() indicate a time-averaged variable.

In this study, metrics have been calculated and presented following two different approaches: (1) with a so-called “time-and-space” approach where metrics are calculated in one step, based on all reanalysis–observation pairs available both across the entire domain (or a given country) and over the entire period 2003–2020 or (2) with a so-called “time-then-space” approach where metrics are first calculated at each station before being combined by taking the median across all stations. In this work framework, time-and-space PCC values do not correspond to spatial or temporal correlations but rather to overall spatiotemporal correlations, while time-then-space PCC values do correspond to temporal correlations, though spatially averaged.

Annual trends, based on monthly averages over the entire domain (considering only cells and days with available observations to allow for fair comparisons) and reported in Sect. , have been computed using seasonal Theil–Sen estimators, which account for seasonal variability. Statistical significance has been analysed through correlated seasonal Mann–Kendall trend tests, considering both seasonality and autocorrelation. For more detailed information on how the annual trends are computed we refer the reader to Appendix . It is worth noting that trends are here computed essentially to evaluate the consistency of the reanalyses against observational data but should not be taken as a reliable estimate of real pollutant trends due to the number of stations not being constant but generally increasing throughout the period of study. Moreover, even if a station has available data over the entire period, its location can also be subject to changes over time.

Overall, CAMSRA reproduces the observed [O3] fairly well, with limited negative bias (-12 %) and reasonable error and correlation (36 % and 0.61, respectively). In comparison, MERRA-2 systematically overestimates [O3] (+34 %) and shows poorer error and correlation (48 % and 0.53, respectively). On average, observed O3 mixing ratios reach a minimum between late autumn and early winter then peak in spring and are followed by persistently high but slowly decreasing O3 levels until reaching a sharp drop in late summer (Fig.  in the Appendix). CAMSRA captures the seasonality of O3 reasonably well, although with negative bias during winter and early spring. Conversely, MERRA-2 substantially underestimates the seasonal amplitude (around 15 ppbv, against more than 20 ppbv in observations and CAMSRA).

Throughout the entire period, the median monthly scale nMB in CAMSRA remains below -20 %, with larger underestimations through the beginning of the period and better results during the last years. The bias displays a clear seasonal pattern, with an important winter and spring deterioration (-21 % and -16 %, respectively) but very limited biases in summer and autumn (-4 % and -1 %, respectively). Such oscillating biases have also been reported by over Europe. Regarding the other metrics, median monthly scale nRMSE in CAMSRA reaches its worst values in winter (36 %) when the PCC is conversely the best (0.71), whereas an opposite behaviour with low nRMSE and poor PCC can be observed in summer (26 % and 0.40, respectively). A strong seasonal variability is also found in MERRA-2 statistics, although limited to nMB and nRMSE, which are worst in autumn (+61 % and +67 %, respectively). While the reasonable PCC obtained over the entire dataset (0.53) is likely driven by the good ability of MERRA-2 to capture the O3 seasonality, the much lower monthly PCC values (oscillating around 0.25) suggest that MERRA-2 represents the intra-monthly variability of daily O3 mixing ratios very poorly over a large part of the domain. Nonetheless, MERRA-2 is able to reproduce the spring peak followed by a slow decrease in [O3] typically seen in European observations during summer. In contrast, CAMSRA completely misses this mid-spring O3 peak, as shown in Fig. . Over 2003–2020, no statistically significant annual trend (estimated as a seasonal Theil–Sen slope) of mean [O3] is observed over Europe, neither in MERRA-2 nor in the observations. However, a significant though low positive increase of +0.23 ppbv yr-1 is found in CAMSRA (Table ), at least partly due to the aforementioned stronger underestimation of O3 during the first years of the period.

The country-level evaluation highlights how CAMSRA outperforms MERRA-2 in every single country across the European continent for every computed statistic, with the greatest differences appearing in Belgium (BE) and the Netherlands (NL) and the smallest ones in Spain (ES) and Portugal (PT). In CAMSRA the nMB remains generally negative, at around -10 %, with several countries showing virtually no bias (e.g. the Netherlands (NL), Turkey (TR) and Sweden (SE)), while MERRA-2 displays values in the range of +30 %–70 %. As for the nRMSE, in CAMSRA it remains constrained between 30 % and 50 % for all evaluated countries, whereas in MERRA-2 it generally remains close to 50 %, even surpassing this value for several countries, such as the Netherlands (NL), Poland (PL), Belgium (BE) and Turkey (TR). In most countries the PCC does not differ considerably between reanalyses, remaining in the range 0.4–0.7 and slightly higher values for CAMSRA. Despite its greater original resolution, MERRA-2 fails to capture the spatial variability of the [O3] field, with highly homogeneous mixing ratio values over land, ranging from 35 to 45 ppbv (Fig. d), likely a result of the lack of accurate ozone sources in the parameterized chemistry and limited sensitivity of OMI measurements to lower tropospheric ozone (note that neither MLS nor OMI provide ozone profile information in the troposphere). A wider range of assimilated products, as seen in Table , and more detailed gas-phase chemistry likely accounts for CAMSRA's better overall performance and greater spatial variability. Nevertheless, we expect the MERRA-2 ozone profile product to be useful for scientific studies that focus on the upper troposphere and the stratosphere, given the high correlations found by against independent ozonesonde data at these altitudes.

evaluated surface O3 against the World Meteorological Office's (WMO) Global Atmosphere Watch (GAW) background stations and noticed slightly higher negative biases in winter (with modified nMB down to -40 %), though based on a different and smaller set of stations (45 GAW stations against 1511 EEA rural background stations gridded into 728 cells here). Over 2003–2018, evaluated CAMSRA surface O3 mixing ratios against the European Monitoring and Evaluation Programme's (EMEP) observations, finding typically negative modified normalized mean biases (MNMBs) within -30 % in winter (driven by underestimated O3 mostly at midlatitudes) but positive ones in summer and autumn up to +15 %. Such an oscillating bias is in good agreement with our results over the European continent. Although satellite O3 measurements are extensively assimilated in CAMSRA (11 space-based O3 products included), already demonstrated their minor impact on surface O3. This may be at least partly due to the relatively low sensitivity of space-borne instruments to lowermost tropospheric O3 (e.g. ). All in all, likely due to a more detailed representation of the tropospheric chemistry, CAMSRA clearly outperforms MERRA-2 in simulating surface O3 mixing ratios.

When considering urban background stations (Table ) the overall nMB in CAMSRA, though shifted in sign, remains very limited (+8 %), whereas MERRA-2 presents an overestimation (+64 %), which nearly doubles the one found in the rural subset. Such an evolution of the statistics at least partly reflects the intrinsic difficulty of coarse reanalyses in representing O3 titration in urban areas. For CAMSRA, the nRMSE shows no significant variation (+34 %), though a slight improvement is found for the PCC (0.72), which represents the best overall correlation across all station subsets and pollutants. Compared to the rural subset, MERRA-2 presents a very similar PCC (0.54), though an important deterioration in the nRMSE is found (+75 %). The overall averaged [O3] is 5.7 ppbv smaller than in the rural station subset.

CAMSRA systematically overestimates the mixing ratio of NO2 (Fig. a) throughout the entire period of study, with an overall moderate positive bias of +26 % (Table ), although the seasonal variability of NO2 is well captured. In contrast, over 2003–2016 reported mostly limited negative biases but based on a very small set of regional background stations (4 GAW stations) against 1460 EEA stations gridded into 609 cells in the present study. Overall, CAMSRA shows a relatively large overall nRMSE (79 %) and reasonable PCC (0.60).

At median monthly scale, biases increase from +12 % in winter to +42 % in summer (Table ). Monthly scale nRMSE and PCC values show substantial seasonal variations, with better performance in winter (nRMSE and PCC of 70 % and 0.60, respectively) and a notable deterioration in summer (92 % and 0.45).

In terms of long-term trends, the significant decrease in [NO2] observed over 2003–2020 (-0.11 ppbv yr-1) is moderately overestimated by the reanalysis (-0.17 ppbv yr-1, i.e. differing by a 1.5 factor). In relative terms, these decreasing mixing ratio trends found for NO2 in the observations and CAMSRA (-2.3 % yr-1 and -2.5 % yr-1, respectively) are close to the -2.0 % yr-1 NOx emission trend reported by the EEA over the period 1990–2019 in its emission inventory report .

Although it has been demonstrated that the COVID-19 pandemic reduced the NO2 levels over Europe in 2020 (), the observed [NO2] time series only shows a limited reduction, given that only rural background stations are retained for the evaluation, and NO2 is a predominantly urban pollutant. The change in CAMSRA appears less pronounced, potentially due to the coarse resolution of the reanalysis but most likely due to CAMSRA following the RCP8.5 for emissions after 2010 .

At a country level (considering only countries with more than five cells containing observations), most nMBs fall roughly between +10 % and +60 %, with the notable exception of Finland (FI) and Turkey (TR), where a moderate underestimation (-15 % and -25 %, respectively) is found. The nRMSE ranges from around 60 % to over 150 %, depending on the country considered. The PCC remains generally around 0.5, though countries with fewer measuring stations available tend to present lower PCC values (Fig. b). Interestingly, virtually no bias is found in the Netherlands (NL), which also displays the lowest error and highest correlation amongst all the countries examined.

The spatial variability of the [NO2] field across the European continent is consistent with the location of dense urban areas (e.g. Paris, Moscow, Barcelona, Oslo, Algiers), highly industrialized regions (e.g. Po River basin, Rhine-Rühr Valley, Silesia) and busy shipping lanes (e.g. Mediterranean, English Channel, Portuguese coastline). In sparsely populated areas, less industrialized regions and the open sea's [NO2] levels remain below 3 or even 1.5 ppbv (Fig. c).

When considering urban background stations, CAMSRA systematically underestimates [NO2] across the European continent (Table ), with an overall strong negative bias (-40 %, Table ), which can be related in all likelihood to its overly coarse spatial resolution that intrinsically prevents a correct representation of urban NO2 hotspots, as well as to the short chemical lifetime of NO2. By evaluating NO2 tropospheric columns against satellite-based observations, and also reported negative biases over Europe, especially during wintertime. Although this contrasts with the numbers obtained for rural background stations, it is in good agreement with our results for the urban subset, though biases are significantly larger here (evaluated against 6921 EEA urban background stations, gridded into 1461 cells). The underestimation becomes more critical in winter (-45 %, Table ) and slightly improves in summer (-33 %). Note that also found a large underestimation of NO2 in winter over South Korea (around -10 ppbv against -2 ppbv in summer). CAMSRA also displays a large nRMSE and moderate PCC (68 % and 0.56, respectively). The seasonality and intra-annual variability of the NO2 mixing ratio fields are both well captured by CAMSRA.

As shown in Fig. a, MERRA-2 systematically underestimates the mixing ratio of CO (overall nMB of -43 %), while CAMSRA reproduces the observed mixing ratio well, with an overall limited mean bias (-12 %). MERRA-2 dramatically fails at reproducing the seasonal variability of CO, with the strongest negative biases in winter (-51 %). Conversely, CAMSRA captures the seasonal cycle well, although negative biases are also somewhat stronger in winter (-15 %). Note that , in their evaluation over South Korea, also reported a severe winter underestimation in CAMSRA together with an absence of variability in surface CO over the period 2003–2018 in MERRA-2. Interestingly, CAMSRA displays a lack of nMB seasonality, with an almost constant value throughout summer, autumn and winter. A likely explanation for this is the good ability of CAMSRA to capture the intra-annual variability of [CO] throughout the year. The overall nRMSE is high in both reanalyses (85 % and 95 %, respectively), with again a lower winter performance in MERRA-2 and an overall absence of seasonality in CAMSRA. evaluated CO in Europe against data from GAW stations over the period 2003–2018, reporting a persistent underestimation (modified nMB ranging from -10 % to -20 %) of surface CO, in agreement with our results. In contrast, reported an overall overestimation of around 10 ppbv for the period 2003–2017, which again could be due to the different set of stations taken into account (15 GAW stations, most of them regional and several of them located at high altitudes).

At monthly scale, the median [CO], nMB and nRMSE in CAMSRA partially capture the seasonality, showing a better performance in autumn (0 %) and summer (31 %) and a moderate springtime (+9 %) and wintertime (39 %) deterioration, respectively. As seen for O3, the PCC follows the opposite behaviour, with better performance in DJF (0.58) and a late springtime deterioration (0.46). In contrast to CAMSRA, MERRA-2 is unable to reproduce the seasonal variability of surface [CO], despite the nMB and nRMSE displaying significant variability throughout the different seasons. A surprisingly large increase in [CO] is found in MERRA-2 throughout 2020. It is unclear what stands behind such a significant increase, but this abrupt change affects mostly specific pollution hotspots in the European continent, including the Rhine-Rühr valley and the Paris and London metropolitan areas, as well as the Po River basin. This [CO] surge is also found in the raw version (i.e. non-regridded) of the reanalysis. The strong statistically significant decrease in CO observed across Europe over 2003–2020 (-3.47 ppbvyr-1) is moderately overestimated in CAMSRA (-4.56 ppbvyr-1), although less dramatically than in MERRA-2, where CO remains roughly constant over all of the study period, displaying a small negative trend (-0.44 ppbvyr-1). In the EEA reports a CO emission trend of -2.3 % yr-1 over 1990–2019, relatively close to the mixing ratio trends found in CAMSRA, -2.4 % yr-1, and the observations, -1.9 % yr-1. In 2020, MERRA-2 shows a very large increase in [CO] across most of Europe, in contrast to both CAMSRA and the observations. The overall PCC in MERRA-2 and CAMSRA is poor (0.22 and 0.28, respectively), although better PCC values (∼ 0.40) are found at monthly scale (0.53 and 0.55, respectively).

This CO underestimation typically spreads over the whole European continent, with strong differences across countries. As CO is not assimilated in MERRA-2 but simulated by the GEOS-5 modelling system, this underestimation likely comes from a poor representation of CO emissions and/or excessively large CO sinks. In both reanalyses, the best scores in terms of bias, PCC and nRMSE are found in Germany (DE) and to a lesser extent in the Netherlands (NL). Conversely, far poorer results are obtained in Poland (PL) and Romania (RO). Although different, the nMB and nRMSE in both reanalyses typically show comparable variations from one country to another. Both CAMSRA and MERRA-2 show CO hotspots over large urban areas and/or highly industrialized regions (e.g. Moscow, Po River basin). However, compared to CAMSRA, MERRA-2 highlights some additional hotspots, for instance on the Vatnajökull ice cap, located in Iceland, a region well known for its sub-glacial volcanoes (e.g. Grímsvötn) which experience frequent degassing. Another significant hotspot is found in the Donets Basin (eastern Ukraine), an important coal mining region. Two other CO hotspots can be seen south and north of Moscow, corresponding to the cities of Voronezh and Yaroslavl, respectively, but it is unlikely that CO levels comparable to those of Moscow are found in these intermediate-sized cities (Fig. c, d).

The reanalyses also differ in the locations where [CO] is higher across Europe (Po River basin in CAMSRA; Rhine-Rühr Valley in MERRA-2). CAMSRA highlights the highest CO mixing ratios in Europe in the Po River basin and displays moderate mixing ratio values in the Rhine-Rühr area, which suggests a longer CO lifetime in the former given that reports the highest CO emissions, over the whole period of 1990–2019, in Germany. Therefore, in sharp contrast to CAMSRA, MERRA-2 obviously fails to capture the chemistry processes of surface CO, with a likely underestimation of emission sources and/or too large CO sinks, thus being unable to reproduce the spatiotemporal variability of surface CO observed over Europe.

From Table  it immediately becomes apparent that the main difference between the urban and rural subsets, aside from the large variation in baseline mixing ratios, comes from CAMSRA largely underestimating the observed [CO] in urban cells, with the nMB (-46 %) nearly quadrupling when compared to the rural evaluation. For MERRA-2 the nMB also suffers from a deterioration (-64 %) but more limited due to an already large bias in the rural subset. For both CAMSRA and MERRA-2, the overall nRMSE (91 % and 105 %, respectively) and PCC (0.39 and 0.19, respectively) remain close to the rural values, with no significant variations. The seasonal behaviour of both reanalyses also remains unchanged, with MERRA-2 completely missing the amplitude of the seasonal cycle. This large amplitude is also the reason why CAMSRA loses its ability to reproduce the observed CO mixing ratio.

When computed over the entire dataset (Table ), the statistics of CAMSRA and MERRA-2 show very poor nRMSE and PCC (around 143 % and 0.33–0.35, respectively) but better performance in terms of bias for CAMSRA (+10 %) than for MERRA-2 (+40 %). On average, the overestimation of MERRA-2 is much higher in winter, meaning the amplitude of the SO2 seasonal cycle is strongly overestimated (Fig. ).

At monthly scale (Fig. a), the median nMB in MERRA-2 severely deteriorates (+75 %) and increases throughout time, with the worst performance peaking in SON (+94 %) and a slight springtime improvement (+57 %). The median monthly scale nMB in CAMSRA tends to improve between late spring and early summer, reaching values close to 0 %, though it oscillates throughout the year, dropping to -12 % in winter and peaking at +11 % in autumn. Note that , though finding a larger [SO2] overestimation over South Korea, greater than the underestimation shown here for Europe, found a similar nMB seasonality, with nMB improving (∼ +2 ppbv) and worsening (∼ +6 ppbv) in warm and cold months, respectively. In MERRA-2 the median nMB oscillates roughly around +69 % (with a ±3 % range), though it suffers from an important increase (with significant intra-annual variability) from 2013 onwards due to a decrease in observed [SO2]. A similar increase is also observed in the nRMSE. The monthly scale nRMSE and PCC remain roughly constant (when averaged across all months) throughout all seasons, both in CAMSRA (around 70 % and 0.28, respectively) and in MERRA-2 (around 108 % and 0.31, respectively), though the latter displays much stronger seasonal variability. Note also the large difference between the monthly scale nRMSE (70 %–108 %) and the overall nRMSE (around 143 %). The statistically significant negative trend found in observed SO2 mixing ratios (-0.034 ppbvyr-1) is largely overestimated by CAMSRA (-0.078 ppbvyr-1) and well reproduced by MERRA-2 (-0.033 ppbvyr-1) (Fig. ). In the EEA reports a SO2 anthropogenic emission trend of -3.2 % yr-1 over 1990–2019, falling between the mixing ratio trend found in CAMSRA, -6.2 % yr-1, and the one found in the observations, -2.7 % yr-1, and MERRA-2, -1.5 % yr-1.

The country-level evaluation for SO2 shows very heterogeneous results across countries, differing substantially from the observed behaviour in previously examined reactive gases. The nMB presents a wide range of variation, with certain countries showing very reduced biases for at least one of the reanalyses (e.g. Portugal, Czech Republic, Austria, Belgium) and others presenting biases well over ±50 % (e.g. the United Kingdom, France, Romania, Switzerland). Both the nRMSE and PCC display a poor performance, ranging roughly within 100 %–150 % and 0.10–0.50, respectively (Fig. b). Upon a first examination of the SO2 spatial distribution, it may appear as if the mixing ratio values in the time series should be larger for CAMSRA, though this is actually misleading, as the evaluation is performed only in cells with available observations. Therefore, regions with a higher station density contribute more towards the final mixing ratio value. From Fig. e it can be immediately seen that MERRA-2 presents higher SO2 mixing ratios in several countries which have an overall larger number of stations (e.g. Germany, the Netherlands, France, Italy).

In both reanalyses, the heterogeneous distribution of [SO2] is consistent with the location of highly industrialized areas (e.g. Po River basin, Rhine-Rühr Valley) and coal mining regions (e.g. Silesia, Donets Basin, Balkans). To a minor extent, there are also significant SO2 mixing ratios in dense urban areas and along shipping lanes. Surprisingly, the aforementioned CO hotspot found in MERRA-2 over the Icelandic Vatnajökull ice cap does not come with an associated SO2 hotspot, which contrasts with the fact that SO2 emissions represent a large fraction of volcanic gases. The reanalyses show sharp differences in the regions where the highest mixing ratios of SO2 are present, with CAMSRA favouring coal mining regions and dense urban areas and MERRA-2 showing a more balanced distribution between them (Fig. c, d, e). Overall, both reanalysis products present distinct although substantial deficiencies in their representation of SO2 mixing ratios, with the increasing overestimation of MERRA-2 probably being the most critical issue. Anthropogenic SO2 emissions in MERRA-2 are obtained from AeroCom Phase II () and EDGAR v4.2 () inventories, with emissions fixed to those of the last year available in each inventory . Thus, the progressive deterioration of the bias in MERRA-2, particularly notorious from 2013 onwards, likely arises due to an emission overestimation which propagates throughout the time period where no updated SO2 emissions are available.

When considering urban background stations, both CAMSRA and MERRA-2 shift towards a moderate negative nMB (-29 % and -26 %, respectively), far from the positive bias found in the rural subset. Overall, both the nRMSE (247 % and 251 %, respectively) and PCC (0.18 and 0.08, respectively) are extremely poor (see Table ). The mixing ratio in CAMSRA presents significant intra-annual variability and thus fails to correctly reproduce the observed seasonal behaviour. MERRA-2 shows a much better ability to capture the seasonality of [SO2], though it still suffers from the increasing overestimation previously highlighted for rural background stations.

Overall, CAMSRA and MERRA-2 reanalyses represent surface PM10 concentrations over Europe (Table ) moderately well, with a limited positive nMB (+14 %) for CAMSRA and moderate bias for MERRA-2 (+29 %) but poor nRMSE (81 % and 129 %, respectively) and PCC (0.45 and 0.22, respectively).

At monthly scale, the median nMB in CAMSRA presents a strong seasonality, with an important deterioration during spring (+36 %) and better performance in DJF (+5 %), while the nRMSE and PCC show a strong and complex intra-annual variability without a clear seasonal pattern (remaining in the range of 53 %–65 % and 0.48–0.54, respectively). In comparison, nRMSE and PCC in MERRA-2 follow a clear seasonal behaviour, with strongly deteriorated results during winter (105 % and 0.11, respectively) but better summertime performance (71 % and 0.41, respectively). Surprisingly, the median nMB in MERRA-2 also peaks in JJA (+38 %), with a small bias reduction in SON and a wintertime low (+24 %). found a slightly positive PM10 bias for CAMSRA in South Korea over 2003–2018, while for MERRA-2 their findings suggest a clear underestimation that worsens significantly in winter, the former being in good agreement with our results over Europe. The statistically significant negative trend present in the observations (-0.36 µgm-3yr-1) is strongly overestimated by CAMSRA (-0.70 µgm-3yr-1) and severely underestimated by MERRA-2 (-0.02 µgm-3yr-1), with the latter not being statistically significant (at a 99 % confidence level). In the EEA reports a PM10 emission trend of -1.7 % yr-1 over 2000–2019, far from the concentration trend of CAMSRA, -3.3 % yr-1, but closer to the one found in the observations, -2.1 % yr-1.

At a country level, CAMSRA tends to outperform MERRA-2 in most countries, with lower nRMSE (50 %–100 % and 75 %–150 %, respectively) and higher PCC values (0.3–0.6 against 0.1–0.4, respectively). The nMB presents a wide range of variation in both reanalyses, with certain countries showing virtually no bias for MERRA-2 (e.g. Austria), for CAMSRA (e.g. Spain, the Netherlands, Portugal) or for both reanalyses (e.g. Poland, Hungary, Slovakia). Other countries present biases well over ±25 % (e.g. Turkey, Germany, Ireland, the United Kingdom). Though MERRA-2 presents lower nMB values than CAMSRA in several countries (e.g. Iceland, Germany, Czech Republic, Belgium), both the nRMSE and PCC point towards a greater performance by CAMSRA in all cases (Fig. b).

Again, despite its finer resolution, MERRA-2 displays a more homogeneous concentration over land in which the multiple PM10 hotspots found in CAMSRA – in industrialized regions (e.g. Po River basin, Silesia) and in certain urban areas (e.g. Paris, Moscow, Madrid) – are missing. In addition, it also shows much higher PM10 concentrations over the open seas and North Africa, where sea salt and dust sources are predominant. It thus seems that Eq. () severely overestimates the surface concentrations of PM10, as shown in Fig. d), with MERRA-2 displaying differences of more than a 100 µgm-3, particularly over desert areas. This overestimation is likely related to sea salt and dust concentrations in the model being overestimated, as shown in the Supplement. Overall, CAMSRA unambiguously outperforms MERRA-2 in capturing the spatiotemporal variability of PM10 surface concentrations over Europe.

As shown in Table , both CAMSRA and MERRA-2 present limited negative nMB (-20 and -8 %, respectively) for the urban subset, which contrasts with the positive bias found for rural stations. For both reanalyses, the overall nRMSE (85 % and 112 %, respectively) and PCC (0.36 and 0.19, respectively) remain close to their rural counterparts, with no significant variations. The observed PM10 concentration is characterized by strong intra-annual variability, though certain seasonality is still present.

MERRA-2 reproduces surface PM2.5 concentrations over Europe (Table ) moderately well, with a low negative nMB (-9 %) but poor nRMSE and PCC (98 % and 0.29, respectively), while CAMSRA presents an overall worst nMB (+14 %), similar nRMSE (96 %) and slightly better but still moderate PCC (0.43).

The median monthly scale nMB in CAMSRA presents a clear seasonal pattern, with the bias heavily deteriorating in MAM and JJA (+41 %) but virtually vanishing in DJF (+1 %). MERRA-2 also shows a clear seasonality, with the largest over- and underestimations occurring during summer (+21 %) and winter (-17 %), respectively. Interestingly, the MERRA-2 and CAMSRA nMB time series, while initially displaying an absolute difference of ∼ 50 %, converge from 2017 onwards. Similarly to the behaviour observed for PM10, the median nRMSE and PCC in CAMSRA show a strong intra-annual variability without a clear seasonal pattern (remaining in the range of 61 %–74 % and 0.48–0.53, respectively). As for MERRA-2, both the nRMSE and the PCC present significant seasonal variability, with better performance in summer (50 % and 0.58, respectively) and a sharp wintertime deterioration (74 % and 0.36, respectively). Similar results are reported by when evaluating MERRA-1 over Europe, with an overall limited negative bias and a deterioration in winter. Note also that evaluated PM2.5 in MERRA-2 against 20 background stations in India, finding a moderate negative nMB (-34 %; -27 µgm-3) and a larger wintertime underestimation, in agreement with our results over Europe. The negative trend present in the observations (-0.10 µgm-3yr-1) has been found to not be statistically significant, though it is strongly overestimated by CAMSRA (-0.23 µgm-3yr-1) and completely missed by MERRA-2. As a consequence, though the nMB time series of CAMSRA and MERRA-2 differ by more than 30 % in 2003, they end up converging progressively along the period 2003–2020. In the EEA reports a PM2.5 emission trend of -1.9 % yr-1 over 2000–2019 which, while not directly comparable to a concentration trend as previously mentioned, is close to the trend found in CAMSRA, -1.7 % yr-1, but far from the one found in the observations, -0.9 % yr-1.

At a country level (Fig. b), the differences in PM2.5 between CAMSRA and MERRA-2 are less pronounced than for PM10, especially for the PCC (with most values in the range 0.3–0.6), and to a lesser extent for the nRMSE (with most values in the range of 60 %–100 %). The nMB presents a similar behaviour to the one observed for PM10, with certain countries showing virtually no bias for CAMSRA (e.g. the Netherlands) or MERRA-2 (e.g. the United Kingdom, France, Germany, Belgium) and other countries presenting important negative/positive biases (e.g. Turkey, Sweden).

The spatial variability of the PM2.5 concentration remains close to the one obtained for PM10 in all regions and in both reanalyses, except over the open seas, where MERRA-2 no longer shows exceedingly large sea salt levels (which thus prevail mostly in the coarse mode). The surface pollution hotspots present in Fig.  are essentially the same ones that appear in Fig. , though a notable exception is observed in MERRA-2 over Iceland. A large PM2.5 concentration peak, also visible for PM10, can be spotted in Iceland's time series during 2010, surpassing 100 µgm-3, likely due to the Eyjafjallajökull volcanic eruption, which emitted very large amounts of volcanic ash .

As for urban background stations, CAMSRA presents an overall small negative nMB (-13 %), while MERRA-2 displays a larger but limited negative bias (-30 %). In terms of nRMSE and PCC, both CAMSRA and MERRA-2 perform rather poorly, with large errors (86 % and 96 %, respectively) and low correlations (0.41 and 0.24, respectively). Similarly to PM10, the observed PM2.5 concentration shows strong intra-annual variability, though a seasonal pattern is also visible.