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<p>Varved lake sediments are exceptional archives of paleoclimatic information due to their precise chronological control and annual resolution. However, quantitative paleoclimate reconstructions based on the biogeochemical composition of biochemical varves are extremely rare, mainly because the climate–proxy relationships are complex and obtaining biogeochemical proxy data at very high (annual) resolution is difficult. Recent developments in high-resolution hyperspectral imaging (HSI) of sedimentary pigment biomarkers combined with micro X-ray fluorescence (<span class="inline-formula">µ</span>XRF) elemental mapping make it possible to measure the structure and composition of varves at unprecedented resolution. This provides opportunities to explore seasonal climate signals preserved in biochemical varves and, thus, assess the potential for annual-resolution climate reconstruction from biochemical varves. Here, we present a geochemical dataset including HSI-inferred sedimentary pigments and <span class="inline-formula">µ</span>XRF-inferred elements at very high spatial resolution (60 <span class="inline-formula">µ</span>m, i.e. <span class="inline-formula"><i>&gt;</i> 100</span> data points per varve year) in varved sediments of Lake Żabińskie, Poland, over the period 1966–2019 CE. We compare these data with local meteorological observations to explore and quantify how changing seasonal meteorological conditions influenced sediment composition and varve formation processes. Based on the dissimilarity of within-varve multivariate geochemical time series, we classified varves into four types. Multivariate analysis of variance shows that these four varve types were formed in years with significantly different seasonal meteorological conditions. Generalized additive models (GAMs) were used to infer seasonal climate conditions based on sedimentary variables. Spring and summer (MAMJJA) temperatures were predicted using Ti and total C (<span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M5" display="inline" overflow="scroll" dspmath="mathml"><mrow><msubsup><mi>R</mi><mi mathvariant="normal">adj</mi><mn mathvariant="normal">2</mn></msubsup><mo>=</mo><mn mathvariant="normal">0.55</mn></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="55pt" height="18pt" class="svg-formula" dspmath="mathimg" md5hash="d9406a3719e7a052d0a0291b91523381"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="cp-17-2055-2021-ie00001.svg" width="55pt" height="18pt" src="cp-17-2055-2021-ie00001.png"/></svg:svg></span></span>; cross-validated root mean square error (CV-RMSE) <span class="inline-formula">=</span> 0.7 <span class="inline-formula">^∘</span>C, 14.4 %). Windy days from March to December (mean daily wind speed <span class="inline-formula"><i>&gt;</i> 7</span> m s<span class="inline-formula">⁻¹</span>) were predicted using mass accumulation rate (MAR) and Si (<span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M10" display="inline" overflow="scroll" dspmath="mathml"><mrow><msubsup><mi>R</mi><mi mathvariant="normal">adj</mi><mn mathvariant="normal">2</mn></msubsup><mo>=</mo><mn mathvariant="normal">0.48</mn></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="55pt" height="18pt" class="svg-formula" dspmath="mathimg" md5hash="7c3fa76fc747fe3f22d7324fb4fa831b"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="cp-17-2055-2021-ie00002.svg" width="55pt" height="18pt" src="cp-17-2055-2021-ie00002.png"/></svg:svg></span></span>; CV-RMSE <span class="inline-formula">=</span> 19.0 %). This study demonstrates that high-resolution scanning techniques are promising tools to improve our understanding of varve formation processes and climate–proxy relationships in biochemical varves. This knowledge is the basis for quantitative high-resolution paleoclimate reconstructions, and here we provide examples of calibration and validation of annual-resolution seasonal weather inference from varve biogeochemical data.</p>